Correlation Between Camellia Metal and Chernan Metal
Can any of the company-specific risk be diversified away by investing in both Camellia Metal and Chernan Metal at the same time? Although using a correlation coefficient on its own may not help to predict future stock returns, this module helps to understand the diversifiable risk of combining Camellia Metal and Chernan Metal into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Camellia Metal Co and Chernan Metal Industrial, you can compare the effects of market volatilities on Camellia Metal and Chernan Metal 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 Camellia Metal with a short position of Chernan Metal. Check out your portfolio center. Please also check ongoing floating volatility patterns of Camellia Metal and Chernan Metal.
Diversification Opportunities for Camellia Metal and Chernan Metal
0.67 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Camellia and Chernan is 0.67. Overlapping area represents the amount of risk that can be diversified away by holding Camellia Metal Co and Chernan Metal Industrial in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Chernan Metal Industrial and Camellia Metal 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 Camellia Metal Co are associated (or correlated) with Chernan Metal. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Chernan Metal Industrial has no effect on the direction of Camellia Metal i.e., Camellia Metal and Chernan Metal go up and down completely randomly.
Pair Corralation between Camellia Metal and Chernan Metal
Assuming the 90 days trading horizon Camellia Metal Co is expected to under-perform the Chernan Metal. But the stock apears to be less risky and, when comparing its historical volatility, Camellia Metal Co is 1.64 times less risky than Chernan Metal. The stock trades about -0.01 of its potential returns per unit of risk. The Chernan Metal Industrial is currently generating about 0.01 of returns per unit of risk over similar time horizon. If you would invest 3,700 in Chernan Metal Industrial on October 5, 2024 and sell it today you would lose (210.00) from holding Chernan Metal Industrial or give up 5.68% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 100.0% |
Values | Daily Returns |
Camellia Metal Co vs. Chernan Metal Industrial
Performance |
Timeline |
Camellia Metal |
Chernan Metal Industrial |
Camellia Metal and Chernan Metal Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Camellia Metal and Chernan Metal
The main advantage of trading using opposite Camellia Metal and Chernan Metal positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Camellia Metal position performs unexpectedly, Chernan Metal 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 Chernan Metal will offset losses from the drop in Chernan Metal's long position.Camellia Metal vs. Catcher Technology Co | Camellia Metal vs. Solar Applied Materials | Camellia Metal vs. Evergreen Steel Corp | Camellia Metal vs. Shin Zu Shing |
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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 Equity Forecasting module to use basic forecasting models to generate price predictions and determine price momentum.
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