Correlation Between Camellia Metal and Amulaire Thermal
Can any of the company-specific risk be diversified away by investing in both Camellia Metal and Amulaire Thermal 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 Amulaire Thermal 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 Amulaire Thermal Technology, you can compare the effects of market volatilities on Camellia Metal and Amulaire Thermal 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 Amulaire Thermal. Check out your portfolio center. Please also check ongoing floating volatility patterns of Camellia Metal and Amulaire Thermal.
Diversification Opportunities for Camellia Metal and Amulaire Thermal
0.53 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between Camellia and Amulaire is 0.53. Overlapping area represents the amount of risk that can be diversified away by holding Camellia Metal Co and Amulaire Thermal Technology in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Amulaire Thermal Tec 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 Amulaire Thermal. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Amulaire Thermal Tec has no effect on the direction of Camellia Metal i.e., Camellia Metal and Amulaire Thermal go up and down completely randomly.
Pair Corralation between Camellia Metal and Amulaire Thermal
Assuming the 90 days trading horizon Camellia Metal Co is expected to generate 0.7 times more return on investment than Amulaire Thermal. However, Camellia Metal Co is 1.42 times less risky than Amulaire Thermal. It trades about 0.0 of its potential returns per unit of risk. Amulaire Thermal Technology is currently generating about -0.09 per unit of risk. If you would invest 1,500 in Camellia Metal Co on September 16, 2024 and sell it today you would lose (10.00) from holding Camellia Metal Co or give up 0.67% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Camellia Metal Co vs. Amulaire Thermal Technology
Performance |
Timeline |
Camellia Metal |
Amulaire Thermal Tec |
Camellia Metal and Amulaire Thermal Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Camellia Metal and Amulaire Thermal
The main advantage of trading using opposite Camellia Metal and Amulaire Thermal positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Camellia Metal position performs unexpectedly, Amulaire Thermal 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 Amulaire Thermal will offset losses from the drop in Amulaire Thermal'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 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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