Correlation Between Central Plains and Guangdong Liantai
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By analyzing existing cross correlation between Central Plains Environment and Guangdong Liantai Environmental, you can compare the effects of market volatilities on Central Plains and Guangdong Liantai 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 Central Plains with a short position of Guangdong Liantai. Check out your portfolio center. Please also check ongoing floating volatility patterns of Central Plains and Guangdong Liantai.
Diversification Opportunities for Central Plains and Guangdong Liantai
0.97 | Correlation Coefficient |
Almost no diversification
The 3 months correlation between Central and Guangdong is 0.97. Overlapping area represents the amount of risk that can be diversified away by holding Central Plains Environment and Guangdong Liantai Environmenta in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Guangdong Liantai and Central Plains 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 Central Plains Environment are associated (or correlated) with Guangdong Liantai. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Guangdong Liantai has no effect on the direction of Central Plains i.e., Central Plains and Guangdong Liantai go up and down completely randomly.
Pair Corralation between Central Plains and Guangdong Liantai
Assuming the 90 days trading horizon Central Plains is expected to generate 1.36 times less return on investment than Guangdong Liantai. But when comparing it to its historical volatility, Central Plains Environment is 1.09 times less risky than Guangdong Liantai. It trades about 0.18 of its potential returns per unit of risk. Guangdong Liantai Environmental is currently generating about 0.22 of returns per unit of risk over similar time horizon. If you would invest 316.00 in Guangdong Liantai Environmental on September 5, 2024 and sell it today you would earn a total of 123.00 from holding Guangdong Liantai Environmental or generate 38.92% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Very Strong |
Accuracy | 100.0% |
Values | Daily Returns |
Central Plains Environment vs. Guangdong Liantai Environmenta
Performance |
Timeline |
Central Plains Envir |
Guangdong Liantai |
Central Plains and Guangdong Liantai Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Central Plains and Guangdong Liantai
The main advantage of trading using opposite Central Plains and Guangdong Liantai positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Central Plains position performs unexpectedly, Guangdong Liantai 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 Liantai will offset losses from the drop in Guangdong Liantai's long position.Central Plains vs. Allmed Medical Products | Central Plains vs. Northking Information Technology | Central Plains vs. Contec Medical Systems | Central Plains vs. Hengkang Medical Group |
Guangdong Liantai vs. Anji Foodstuff Co | Guangdong Liantai vs. Ningbo Ligong Online | Guangdong Liantai vs. Hunan Mendale Hometextile | Guangdong Liantai vs. Fiberhome Telecommunication Technologies |
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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