Correlation Between ACM Research and Guangdong Cellwise
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By analyzing existing cross correlation between ACM Research Shanghai and Guangdong Cellwise Microelectronics, you can compare the effects of market volatilities on ACM Research and Guangdong Cellwise 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 ACM Research with a short position of Guangdong Cellwise. Check out your portfolio center. Please also check ongoing floating volatility patterns of ACM Research and Guangdong Cellwise.
Diversification Opportunities for ACM Research and Guangdong Cellwise
0.87 | Correlation Coefficient |
Very poor diversification
The 3 months correlation between ACM and Guangdong is 0.87. Overlapping area represents the amount of risk that can be diversified away by holding ACM Research Shanghai and Guangdong Cellwise Microelectr in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Guangdong Cellwise and ACM Research 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 ACM Research Shanghai are associated (or correlated) with Guangdong Cellwise. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Guangdong Cellwise has no effect on the direction of ACM Research i.e., ACM Research and Guangdong Cellwise go up and down completely randomly.
Pair Corralation between ACM Research and Guangdong Cellwise
Assuming the 90 days trading horizon ACM Research is expected to generate 1.83 times less return on investment than Guangdong Cellwise. But when comparing it to its historical volatility, ACM Research Shanghai is 1.24 times less risky than Guangdong Cellwise. It trades about 0.14 of its potential returns per unit of risk. Guangdong Cellwise Microelectronics is currently generating about 0.2 of returns per unit of risk over similar time horizon. If you would invest 2,980 in Guangdong Cellwise Microelectronics on September 22, 2024 and sell it today you would earn a total of 2,391 from holding Guangdong Cellwise Microelectronics or generate 80.23% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Strong |
Accuracy | 100.0% |
Values | Daily Returns |
ACM Research Shanghai vs. Guangdong Cellwise Microelectr
Performance |
Timeline |
ACM Research Shanghai |
Guangdong Cellwise |
ACM Research and Guangdong Cellwise Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with ACM Research and Guangdong Cellwise
The main advantage of trading using opposite ACM Research and Guangdong Cellwise positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if ACM Research position performs unexpectedly, Guangdong Cellwise 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 Guangdong Cellwise will offset losses from the drop in Guangdong Cellwise's long position.ACM Research vs. Huasi Agricultural Development | ACM Research vs. Hengdian Entertainment Co | ACM Research vs. Long Yuan Construction | ACM Research vs. Shantui Construction Machinery |
Guangdong Cellwise vs. Nanjing Putian Telecommunications | Guangdong Cellwise vs. Tianjin Realty Development | Guangdong Cellwise vs. Kangyue Technology Co | Guangdong Cellwise vs. Shenzhen Hifuture Electric |
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 Watchlist Optimization module to optimize watchlists to build efficient portfolios or rebalance existing positions based on the mean-variance optimization algorithm.
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