Correlation Between US Global and Uber Technologies
Can any of the company-specific risk be diversified away by investing in both US Global and Uber Technologies 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 US Global and Uber Technologies into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between US Global Investors and Uber Technologies, you can compare the effects of market volatilities on US Global and Uber Technologies 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 US Global with a short position of Uber Technologies. Check out your portfolio center. Please also check ongoing floating volatility patterns of US Global and Uber Technologies.
Diversification Opportunities for US Global and Uber Technologies
-0.35 | Correlation Coefficient |
Very good diversification
The 3 months correlation between GROW and Uber is -0.35. Overlapping area represents the amount of risk that can be diversified away by holding US Global Investors and Uber Technologies in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Uber Technologies and US Global 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 US Global Investors are associated (or correlated) with Uber Technologies. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Uber Technologies has no effect on the direction of US Global i.e., US Global and Uber Technologies go up and down completely randomly.
Pair Corralation between US Global and Uber Technologies
Given the investment horizon of 90 days US Global Investors is expected to under-perform the Uber Technologies. But the stock apears to be less risky and, when comparing its historical volatility, US Global Investors is 2.1 times less risky than Uber Technologies. The stock trades about -0.02 of its potential returns per unit of risk. The Uber Technologies is currently generating about 0.05 of returns per unit of risk over similar time horizon. If you would invest 6,455 in Uber Technologies on September 2, 2024 and sell it today you would earn a total of 741.00 from holding Uber Technologies or generate 11.48% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
US Global Investors vs. Uber Technologies
Performance |
Timeline |
US Global Investors |
Uber Technologies |
US Global and Uber Technologies Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with US Global and Uber Technologies
The main advantage of trading using opposite US Global and Uber Technologies positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if US Global position performs unexpectedly, Uber Technologies 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 Uber Technologies will offset losses from the drop in Uber Technologies' long position.US Global vs. Gladstone Investment | US Global vs. PennantPark Floating Rate | US Global vs. Horizon Technology Finance | US Global vs. Stellus Capital Investment |
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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 Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.
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