Correlation Between COMBA TELECOM and China Merchants
Can any of the company-specific risk be diversified away by investing in both COMBA TELECOM and China Merchants 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 COMBA TELECOM and China Merchants into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between COMBA TELECOM SYST and China Merchants Port, you can compare the effects of market volatilities on COMBA TELECOM and China Merchants 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 COMBA TELECOM with a short position of China Merchants. Check out your portfolio center. Please also check ongoing floating volatility patterns of COMBA TELECOM and China Merchants.
Diversification Opportunities for COMBA TELECOM and China Merchants
0.58 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between COMBA and China is 0.58. Overlapping area represents the amount of risk that can be diversified away by holding COMBA TELECOM SYST and China Merchants Port in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on China Merchants Port and COMBA TELECOM 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 COMBA TELECOM SYST are associated (or correlated) with China Merchants. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of China Merchants Port has no effect on the direction of COMBA TELECOM i.e., COMBA TELECOM and China Merchants go up and down completely randomly.
Pair Corralation between COMBA TELECOM and China Merchants
Assuming the 90 days trading horizon COMBA TELECOM is expected to generate 1.34 times less return on investment than China Merchants. But when comparing it to its historical volatility, COMBA TELECOM SYST is 1.76 times less risky than China Merchants. It trades about 0.22 of its potential returns per unit of risk. China Merchants Port is currently generating about 0.17 of returns per unit of risk over similar time horizon. If you would invest 129.00 in China Merchants Port on April 23, 2025 and sell it today you would earn a total of 30.00 from holding China Merchants Port or generate 23.26% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Weak |
Accuracy | 100.0% |
Values | Daily Returns |
COMBA TELECOM SYST vs. China Merchants Port
Performance |
Timeline |
COMBA TELECOM SYST |
China Merchants Port |
COMBA TELECOM and China Merchants Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with COMBA TELECOM and China Merchants
The main advantage of trading using opposite COMBA TELECOM and China Merchants positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if COMBA TELECOM position performs unexpectedly, China Merchants 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 China Merchants will offset losses from the drop in China Merchants' long position.COMBA TELECOM vs. SOFI TECHNOLOGIES | COMBA TELECOM vs. Amkor Technology | COMBA TELECOM vs. Agilent Technologies | COMBA TELECOM vs. Insurance Australia Group |
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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 Headlines Timeline module to stay connected to all market stories and filter out noise. Drill down to analyze hype elasticity.
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