Correlation Between Duluth Holdings and Uber Technologies
Can any of the company-specific risk be diversified away by investing in both Duluth Holdings 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 Duluth Holdings and Uber Technologies into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Duluth Holdings and Uber Technologies, you can compare the effects of market volatilities on Duluth Holdings 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 Duluth Holdings with a short position of Uber Technologies. Check out your portfolio center. Please also check ongoing floating volatility patterns of Duluth Holdings and Uber Technologies.
Diversification Opportunities for Duluth Holdings and Uber Technologies
-0.2 | Correlation Coefficient |
Good diversification
The 3 months correlation between Duluth and Uber is -0.2. Overlapping area represents the amount of risk that can be diversified away by holding Duluth Holdings and Uber Technologies in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Uber Technologies and Duluth Holdings 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 Duluth Holdings 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 Duluth Holdings i.e., Duluth Holdings and Uber Technologies go up and down completely randomly.
Pair Corralation between Duluth Holdings and Uber Technologies
Given the investment horizon of 90 days Duluth Holdings is expected to under-perform the Uber Technologies. But the stock apears to be less risky and, when comparing its historical volatility, Duluth Holdings is 1.15 times less risky than Uber Technologies. The stock trades about -0.21 of its potential returns per unit of risk. The Uber Technologies is currently generating about 0.04 of returns per unit of risk over similar time horizon. If you would invest 7,307 in Uber Technologies on December 2, 2024 and sell it today you would earn a total of 294.00 from holding Uber Technologies or generate 4.02% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
Duluth Holdings vs. Uber Technologies
Performance |
Timeline |
Duluth Holdings |
Uber Technologies |
Duluth Holdings and Uber Technologies Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Duluth Holdings and Uber Technologies
The main advantage of trading using opposite Duluth Holdings and Uber Technologies positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Duluth Holdings 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.Duluth Holdings vs. Zumiez Inc | Duluth Holdings vs. JJill Inc | Duluth Holdings vs. Shoe Carnival | Duluth Holdings vs. Cato Corporation |
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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 Rebalancing module to analyze risk-adjusted returns against different time horizons to find asset-allocation targets.
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