Correlation Between Ashford Hospitality and Target
Can any of the company-specific risk be diversified away by investing in both Ashford Hospitality and Target 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 Ashford Hospitality and Target into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Ashford Hospitality Trust and Target, you can compare the effects of market volatilities on Ashford Hospitality and Target 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 Ashford Hospitality with a short position of Target. Check out your portfolio center. Please also check ongoing floating volatility patterns of Ashford Hospitality and Target.
Diversification Opportunities for Ashford Hospitality and Target
0.84 | Correlation Coefficient |
Very poor diversification
The 3 months correlation between Ashford and Target is 0.84. Overlapping area represents the amount of risk that can be diversified away by holding Ashford Hospitality Trust and Target in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Target and Ashford Hospitality 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 Ashford Hospitality Trust are associated (or correlated) with Target. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Target has no effect on the direction of Ashford Hospitality i.e., Ashford Hospitality and Target go up and down completely randomly.
Pair Corralation between Ashford Hospitality and Target
Assuming the 90 days trading horizon Ashford Hospitality is expected to generate 2.43 times less return on investment than Target. In addition to that, Ashford Hospitality is 1.78 times more volatile than Target. It trades about 0.01 of its total potential returns per unit of risk. Target is currently generating about 0.03 per unit of volatility. If you would invest 15,202 in Target on January 26, 2024 and sell it today you would earn a total of 1,332 from holding Target or generate 8.76% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Strong |
| Accuracy | 100.0% |
| Values | Daily Returns |
Ashford Hospitality Trust vs. Target
Performance |
| Timeline |
| Ashford Hospitality Trust |
| Target |
Ashford Hospitality and Target Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Ashford Hospitality and Target
The main advantage of trading using opposite Ashford Hospitality and Target positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Ashford Hospitality position performs unexpectedly, Target 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 Target will offset losses from the drop in Target's long position.| Ashford Hospitality vs. Braemar Hotels Resorts | Ashford Hospitality vs. Braemar Hotels Resorts | Ashford Hospitality vs. Summit Hotel Properties | Ashford Hospitality vs. Aspen Digital |
| Target vs. Costco Wholesale Corp | Target vs. BJs Wholesale Club | Target vs. Dollar Tree | Target vs. Dollar General |
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 Watchlist Optimization module to optimize watchlists to build efficient portfolios or rebalance existing positions based on the mean-variance optimization algorithm.
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