Correlation Between Dine Brands and Uber Technologies
Can any of the company-specific risk be diversified away by investing in both Dine Brands 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 Dine Brands and Uber Technologies into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Dine Brands Global and Uber Technologies, you can compare the effects of market volatilities on Dine Brands 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 Dine Brands with a short position of Uber Technologies. Check out your portfolio center. Please also check ongoing floating volatility patterns of Dine Brands and Uber Technologies.
Diversification Opportunities for Dine Brands and Uber Technologies
-0.13 | Correlation Coefficient |
Good diversification
The 3 months correlation between Dine and Uber is -0.13. Overlapping area represents the amount of risk that can be diversified away by holding Dine Brands Global and Uber Technologies in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Uber Technologies and Dine Brands 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 Dine Brands Global 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 Dine Brands i.e., Dine Brands and Uber Technologies go up and down completely randomly.
Pair Corralation between Dine Brands and Uber Technologies
Considering the 90-day investment horizon Dine Brands is expected to generate 29.29 times less return on investment than Uber Technologies. In addition to that, Dine Brands is 1.23 times more volatile than Uber Technologies. It trades about 0.0 of its total potential returns per unit of risk. Uber Technologies is currently generating about 0.05 per unit of volatility. 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 |
Dine Brands Global vs. Uber Technologies
Performance |
| Timeline |
| Dine Brands Global |
| Uber Technologies |
Dine Brands and Uber Technologies Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Dine Brands and Uber Technologies
The main advantage of trading using opposite Dine Brands and Uber Technologies positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Dine Brands 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.| Dine Brands vs. Bloomin Brands | Dine Brands vs. BJs Restaurants | Dine Brands vs. The Cheesecake Factory | Dine Brands vs. Brinker International |
| Uber Technologies vs. Zoom Video Communications | Uber Technologies vs. Snowflake | Uber Technologies vs. Workday | Uber Technologies vs. C3 Ai Inc |
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 My Watchlist Analysis module to analyze my current watchlist and to refresh optimization strategy. Macroaxis watchlist is based on self-learning algorithm to remember stocks you like.
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