Correlation Between Guangzhou KingTeller and Fujian Newland
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By analyzing existing cross correlation between Guangzhou KingTeller Technology and Fujian Newland Computer, you can compare the effects of market volatilities on Guangzhou KingTeller and Fujian Newland 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 Guangzhou KingTeller with a short position of Fujian Newland. Check out your portfolio center. Please also check ongoing floating volatility patterns of Guangzhou KingTeller and Fujian Newland.
Diversification Opportunities for Guangzhou KingTeller and Fujian Newland
0.96 | Correlation Coefficient |
Almost no diversification
The 3 months correlation between Guangzhou and Fujian is 0.96. Overlapping area represents the amount of risk that can be diversified away by holding Guangzhou KingTeller Technolog and Fujian Newland Computer in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Fujian Newland Computer and Guangzhou KingTeller 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 Guangzhou KingTeller Technology are associated (or correlated) with Fujian Newland. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Fujian Newland Computer has no effect on the direction of Guangzhou KingTeller i.e., Guangzhou KingTeller and Fujian Newland go up and down completely randomly.
Pair Corralation between Guangzhou KingTeller and Fujian Newland
Assuming the 90 days trading horizon Guangzhou KingTeller is expected to generate 1.21 times less return on investment than Fujian Newland. In addition to that, Guangzhou KingTeller is 1.06 times more volatile than Fujian Newland Computer. It trades about 0.2 of its total potential returns per unit of risk. Fujian Newland Computer is currently generating about 0.26 per unit of volatility. If you would invest 1,220 in Fujian Newland Computer on September 2, 2024 and sell it today you would earn a total of 917.00 from holding Fujian Newland Computer or generate 75.16% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Very Strong |
Accuracy | 100.0% |
Values | Daily Returns |
Guangzhou KingTeller Technolog vs. Fujian Newland Computer
Performance |
Timeline |
Guangzhou KingTeller |
Fujian Newland Computer |
Guangzhou KingTeller and Fujian Newland Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Guangzhou KingTeller and Fujian Newland
The main advantage of trading using opposite Guangzhou KingTeller and Fujian Newland positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Guangzhou KingTeller position performs unexpectedly, Fujian Newland 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 Fujian Newland will offset losses from the drop in Fujian Newland's long position.The idea behind Guangzhou KingTeller Technology and Fujian Newland Computer pairs trading is to make the combined position market-neutral, meaning the overall market's direction will not affect its win or loss (or potential downside or upside). This can be achieved by designing a pairs trade with two highly correlated stocks or equities that operate in a similar space or sector, making it possible to obtain profits through simple and relatively low-risk investment.
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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 Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.
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