Correlation Between Xponential Fitness and Weibo Corp
Can any of the company-specific risk be diversified away by investing in both Xponential Fitness and Weibo Corp 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 Xponential Fitness and Weibo Corp into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Xponential Fitness and Weibo Corp, you can compare the effects of market volatilities on Xponential Fitness and Weibo Corp 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 Xponential Fitness with a short position of Weibo Corp. Check out your portfolio center. Please also check ongoing floating volatility patterns of Xponential Fitness and Weibo Corp.
Diversification Opportunities for Xponential Fitness and Weibo Corp
-0.22 | Correlation Coefficient |
Very good diversification
The 3 months correlation between Xponential and Weibo is -0.22. Overlapping area represents the amount of risk that can be diversified away by holding Xponential Fitness and Weibo Corp in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Weibo Corp and Xponential Fitness 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 Xponential Fitness are associated (or correlated) with Weibo Corp. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Weibo Corp has no effect on the direction of Xponential Fitness i.e., Xponential Fitness and Weibo Corp go up and down completely randomly.
Pair Corralation between Xponential Fitness and Weibo Corp
Given the investment horizon of 90 days Xponential Fitness is expected to generate 1.88 times more return on investment than Weibo Corp. However, Xponential Fitness is 1.88 times more volatile than Weibo Corp. It trades about 0.06 of its potential returns per unit of risk. Weibo Corp is currently generating about 0.01 per unit of risk. If you would invest 1,217 in Xponential Fitness on September 3, 2024 and sell it today you would earn a total of 307.00 from holding Xponential Fitness or generate 25.23% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Insignificant |
| Accuracy | 100.0% |
| Values | Daily Returns |
Xponential Fitness vs. Weibo Corp
Performance |
| Timeline |
| Xponential Fitness |
| Weibo Corp |
Xponential Fitness and Weibo Corp Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Xponential Fitness and Weibo Corp
The main advantage of trading using opposite Xponential Fitness and Weibo Corp positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Xponential Fitness position performs unexpectedly, Weibo Corp 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 Weibo Corp will offset losses from the drop in Weibo Corp's long position.| Xponential Fitness vs. Planet Fitness | Xponential Fitness vs. JAKKS Pacific | Xponential Fitness vs. Mattel Inc | Xponential Fitness vs. OneSpaWorld Holdings |
| Weibo Corp vs. YY Inc Class | Weibo Corp vs. DouYu International Holdings | Weibo Corp vs. Tencent Music Entertainment | Weibo Corp vs. Autohome |
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 Pattern Recognition module to use different Pattern Recognition models to time the market across multiple global exchanges.
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