Correlation Between Geely Automobile and CSSC Offshore
Can any of the company-specific risk be diversified away by investing in both Geely Automobile and CSSC Offshore 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 CSSC Offshore 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 CSSC Offshore Marine, you can compare the effects of market volatilities on Geely Automobile and CSSC Offshore 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 CSSC Offshore. Check out your portfolio center. Please also check ongoing floating volatility patterns of Geely Automobile and CSSC Offshore.
Diversification Opportunities for Geely Automobile and CSSC Offshore
-0.09 | Correlation Coefficient |
Good diversification
The 3 months correlation between Geely and CSSC is -0.09. Overlapping area represents the amount of risk that can be diversified away by holding Geely Automobile Holdings and CSSC Offshore Marine in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on CSSC Offshore Marine 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 CSSC Offshore. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of CSSC Offshore Marine has no effect on the direction of Geely Automobile i.e., Geely Automobile and CSSC Offshore go up and down completely randomly.
Pair Corralation between Geely Automobile and CSSC Offshore
Assuming the 90 days horizon Geely Automobile Holdings is expected to generate 1.61 times more return on investment than CSSC Offshore. However, Geely Automobile is 1.61 times more volatile than CSSC Offshore Marine. It trades about 0.16 of its potential returns per unit of risk. CSSC Offshore Marine is currently generating about 0.02 per unit of risk. If you would invest 175.00 in Geely Automobile Holdings on July 6, 2025 and sell it today you would earn a total of 39.00 from holding Geely Automobile Holdings or generate 22.29% 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. CSSC Offshore Marine
Performance |
Timeline |
Geely Automobile Holdings |
CSSC Offshore Marine |
Geely Automobile and CSSC Offshore Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Geely Automobile and CSSC Offshore
The main advantage of trading using opposite Geely Automobile and CSSC Offshore positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Geely Automobile position performs unexpectedly, CSSC Offshore 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 CSSC Offshore will offset losses from the drop in CSSC Offshore's long position.Geely Automobile vs. Boyd Gaming | Geely Automobile vs. ATRESMEDIA | Geely Automobile vs. Tencent Music Entertainment | Geely Automobile vs. Gaming and Leisure |
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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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