Correlation Between Gruppo Mutuionline and ZhongAn Online
Can any of the company-specific risk be diversified away by investing in both Gruppo Mutuionline and ZhongAn Online 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 Gruppo Mutuionline and ZhongAn Online into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Gruppo Mutuionline SpA and ZhongAn Online P, you can compare the effects of market volatilities on Gruppo Mutuionline and ZhongAn Online 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 Gruppo Mutuionline with a short position of ZhongAn Online. Check out your portfolio center. Please also check ongoing floating volatility patterns of Gruppo Mutuionline and ZhongAn Online.
Diversification Opportunities for Gruppo Mutuionline and ZhongAn Online
-0.44 | Correlation Coefficient |
Very good diversification
The 3 months correlation between Gruppo and ZhongAn is -0.44. Overlapping area represents the amount of risk that can be diversified away by holding Gruppo Mutuionline SpA and ZhongAn Online P in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on ZhongAn Online P and Gruppo Mutuionline 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 Gruppo Mutuionline SpA are associated (or correlated) with ZhongAn Online. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of ZhongAn Online P has no effect on the direction of Gruppo Mutuionline i.e., Gruppo Mutuionline and ZhongAn Online go up and down completely randomly.
Pair Corralation between Gruppo Mutuionline and ZhongAn Online
Assuming the 90 days trading horizon Gruppo Mutuionline SpA is expected to generate 0.71 times more return on investment than ZhongAn Online. However, Gruppo Mutuionline SpA is 1.42 times less risky than ZhongAn Online. It trades about 0.09 of its potential returns per unit of risk. ZhongAn Online P is currently generating about -0.12 per unit of risk. If you would invest 3,260 in Gruppo Mutuionline SpA on October 25, 2024 and sell it today you would earn a total of 305.00 from holding Gruppo Mutuionline SpA or generate 9.36% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Very Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Gruppo Mutuionline SpA vs. ZhongAn Online P
Performance |
Timeline |
Gruppo Mutuionline SpA |
ZhongAn Online P |
Gruppo Mutuionline and ZhongAn Online Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Gruppo Mutuionline and ZhongAn Online
The main advantage of trading using opposite Gruppo Mutuionline and ZhongAn Online positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Gruppo Mutuionline position performs unexpectedly, ZhongAn Online 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 ZhongAn Online will offset losses from the drop in ZhongAn Online's long position.Gruppo Mutuionline vs. CENTURIA OFFICE REIT | Gruppo Mutuionline vs. IMAGIN MEDICAL INC | Gruppo Mutuionline vs. CompuGroup Medical SE | Gruppo Mutuionline vs. Genertec Universal Medical |
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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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