Correlation Between Miracll Chemicals and Jiangnan Mould
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By analyzing existing cross correlation between Miracll Chemicals Co and Jiangnan Mould Plastic, you can compare the effects of market volatilities on Miracll Chemicals and Jiangnan Mould 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 Miracll Chemicals with a short position of Jiangnan Mould. Check out your portfolio center. Please also check ongoing floating volatility patterns of Miracll Chemicals and Jiangnan Mould.
Diversification Opportunities for Miracll Chemicals and Jiangnan Mould
0.75 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Miracll and Jiangnan is 0.75. Overlapping area represents the amount of risk that can be diversified away by holding Miracll Chemicals Co and Jiangnan Mould Plastic in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Jiangnan Mould Plastic and Miracll Chemicals 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 Miracll Chemicals Co are associated (or correlated) with Jiangnan Mould. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Jiangnan Mould Plastic has no effect on the direction of Miracll Chemicals i.e., Miracll Chemicals and Jiangnan Mould go up and down completely randomly.
Pair Corralation between Miracll Chemicals and Jiangnan Mould
Assuming the 90 days trading horizon Miracll Chemicals Co is expected to generate 1.42 times more return on investment than Jiangnan Mould. However, Miracll Chemicals is 1.42 times more volatile than Jiangnan Mould Plastic. It trades about 0.23 of its potential returns per unit of risk. Jiangnan Mould Plastic is currently generating about 0.21 per unit of risk. If you would invest 1,542 in Miracll Chemicals Co on November 7, 2024 and sell it today you would earn a total of 135.00 from holding Miracll Chemicals Co or generate 8.75% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Significant |
| Accuracy | 100.0% |
| Values | Daily Returns |
Miracll Chemicals Co vs. Jiangnan Mould Plastic
Performance |
| Timeline |
| Miracll Chemicals |
Risk-Adjusted Performance
0 of 100
Weak | Strong |
Very Weak
| Jiangnan Mould Plastic |
Risk-Adjusted Performance
0 of 100
Weak | Strong |
Very Weak
Miracll Chemicals and Jiangnan Mould Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Miracll Chemicals and Jiangnan Mould
The main advantage of trading using opposite Miracll Chemicals and Jiangnan Mould positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Miracll Chemicals position performs unexpectedly, Jiangnan Mould 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 Jiangnan Mould will offset losses from the drop in Jiangnan Mould's long position.The idea behind Miracll Chemicals Co and Jiangnan Mould Plastic 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.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 Equity Forecasting module to use basic forecasting models to generate price predictions and determine price momentum.
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