Correlation Between Chengdu Xinzhu and Shanghai Metersbonwe
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By analyzing existing cross correlation between Chengdu Xinzhu RoadBridge and Shanghai Metersbonwe FashionAccessories, you can compare the effects of market volatilities on Chengdu Xinzhu and Shanghai Metersbonwe 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 Chengdu Xinzhu with a short position of Shanghai Metersbonwe. Check out your portfolio center. Please also check ongoing floating volatility patterns of Chengdu Xinzhu and Shanghai Metersbonwe.
Diversification Opportunities for Chengdu Xinzhu and Shanghai Metersbonwe
0.87 | Correlation Coefficient |
Very poor diversification
The 3 months correlation between Chengdu and Shanghai is 0.87. Overlapping area represents the amount of risk that can be diversified away by holding Chengdu Xinzhu RoadBridge and Shanghai Metersbonwe FashionAc in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Shanghai Metersbonwe and Chengdu Xinzhu 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 Chengdu Xinzhu RoadBridge are associated (or correlated) with Shanghai Metersbonwe. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Shanghai Metersbonwe has no effect on the direction of Chengdu Xinzhu i.e., Chengdu Xinzhu and Shanghai Metersbonwe go up and down completely randomly.
Pair Corralation between Chengdu Xinzhu and Shanghai Metersbonwe
Assuming the 90 days trading horizon Chengdu Xinzhu is expected to generate 1.69 times less return on investment than Shanghai Metersbonwe. But when comparing it to its historical volatility, Chengdu Xinzhu RoadBridge is 1.28 times less risky than Shanghai Metersbonwe. It trades about 0.16 of its potential returns per unit of risk. Shanghai Metersbonwe FashionAccessories is currently generating about 0.21 of returns per unit of risk over similar time horizon. If you would invest 136.00 in Shanghai Metersbonwe FashionAccessories on September 22, 2024 and sell it today you would earn a total of 100.00 from holding Shanghai Metersbonwe FashionAccessories or generate 73.53% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Strong |
Accuracy | 100.0% |
Values | Daily Returns |
Chengdu Xinzhu RoadBridge vs. Shanghai Metersbonwe FashionAc
Performance |
Timeline |
Chengdu Xinzhu RoadBridge |
Shanghai Metersbonwe |
Chengdu Xinzhu and Shanghai Metersbonwe Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Chengdu Xinzhu and Shanghai Metersbonwe
The main advantage of trading using opposite Chengdu Xinzhu and Shanghai Metersbonwe positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Chengdu Xinzhu position performs unexpectedly, Shanghai Metersbonwe 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 Shanghai Metersbonwe will offset losses from the drop in Shanghai Metersbonwe's long position.Chengdu Xinzhu vs. Cultural Investment Holdings | Chengdu Xinzhu vs. Gome Telecom Equipment | Chengdu Xinzhu vs. Holitech Technology Co | Chengdu Xinzhu vs. Zotye Automobile Co |
Shanghai Metersbonwe vs. Industrial and Commercial | Shanghai Metersbonwe vs. Agricultural Bank of | Shanghai Metersbonwe vs. China Construction Bank | Shanghai Metersbonwe vs. Bank of China |
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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