Correlation Between Geely Automobile and ZhongAn Online
Can any of the company-specific risk be diversified away by investing in both Geely Automobile 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 Geely Automobile and ZhongAn Online into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Geely Automobile Holdings and ZhongAn Online P, you can compare the effects of market volatilities on Geely Automobile 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 Geely Automobile with a short position of ZhongAn Online. Check out your portfolio center. Please also check ongoing floating volatility patterns of Geely Automobile and ZhongAn Online.
Diversification Opportunities for Geely Automobile and ZhongAn Online
-0.3 | Correlation Coefficient |
Very good diversification
The 3 months correlation between Geely and ZhongAn is -0.3. Overlapping area represents the amount of risk that can be diversified away by holding Geely Automobile Holdings 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 Geely Automobile 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 Geely Automobile Holdings 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 Geely Automobile i.e., Geely Automobile and ZhongAn Online go up and down completely randomly.
Pair Corralation between Geely Automobile and ZhongAn Online
Assuming the 90 days horizon Geely Automobile Holdings is expected to generate 0.99 times more return on investment than ZhongAn Online. However, Geely Automobile Holdings is 1.01 times less risky than ZhongAn Online. It trades about 0.03 of its potential returns per unit of risk. ZhongAn Online P is currently generating about -0.06 per unit of risk. If you would invest 172.00 in Geely Automobile Holdings on October 23, 2024 and sell it today you would earn a total of 6.00 from holding Geely Automobile Holdings or generate 3.49% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
Geely Automobile Holdings vs. ZhongAn Online P
Performance |
Timeline |
Geely Automobile Holdings |
ZhongAn Online P |
Geely Automobile and ZhongAn Online Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Geely Automobile and ZhongAn Online
The main advantage of trading using opposite Geely Automobile and ZhongAn Online positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Geely Automobile 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.Geely Automobile vs. WisdomTree Investments | Geely Automobile vs. Casio Computer CoLtd | Geely Automobile vs. SEI INVESTMENTS | Geely Automobile vs. MOVIE GAMES SA |
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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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