Correlation Between GREENX METALS and CDL INVESTMENT
Can any of the company-specific risk be diversified away by investing in both GREENX METALS and CDL INVESTMENT 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 GREENX METALS and CDL INVESTMENT into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between GREENX METALS LTD and CDL INVESTMENT, you can compare the effects of market volatilities on GREENX METALS and CDL INVESTMENT 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 GREENX METALS with a short position of CDL INVESTMENT. Check out your portfolio center. Please also check ongoing floating volatility patterns of GREENX METALS and CDL INVESTMENT.
Diversification Opportunities for GREENX METALS and CDL INVESTMENT
-0.38 | Correlation Coefficient |
Very good diversification
The 3 months correlation between GREENX and CDL is -0.38. Overlapping area represents the amount of risk that can be diversified away by holding GREENX METALS LTD and CDL INVESTMENT in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on CDL INVESTMENT and GREENX METALS 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 GREENX METALS LTD are associated (or correlated) with CDL INVESTMENT. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of CDL INVESTMENT has no effect on the direction of GREENX METALS i.e., GREENX METALS and CDL INVESTMENT go up and down completely randomly.
Pair Corralation between GREENX METALS and CDL INVESTMENT
Assuming the 90 days trading horizon GREENX METALS LTD is expected to generate 2.36 times more return on investment than CDL INVESTMENT. However, GREENX METALS is 2.36 times more volatile than CDL INVESTMENT. It trades about 0.02 of its potential returns per unit of risk. CDL INVESTMENT is currently generating about -0.06 per unit of risk. If you would invest 39.00 in GREENX METALS LTD on August 25, 2024 and sell it today you would earn a total of 0.00 from holding GREENX METALS LTD or generate 0.0% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Insignificant |
| Accuracy | 100.0% |
| Values | Daily Returns |
GREENX METALS LTD vs. CDL INVESTMENT
Performance |
| Timeline |
| GREENX METALS LTD |
| CDL INVESTMENT |
GREENX METALS and CDL INVESTMENT Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with GREENX METALS and CDL INVESTMENT
The main advantage of trading using opposite GREENX METALS and CDL INVESTMENT positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if GREENX METALS position performs unexpectedly, CDL INVESTMENT 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 CDL INVESTMENT will offset losses from the drop in CDL INVESTMENT's long position.| GREENX METALS vs. PERENNIAL ENERGY HD 01 | GREENX METALS vs. Superior Plus Corp | GREENX METALS vs. NMI Holdings | GREENX METALS vs. Origin Agritech |
| CDL INVESTMENT vs. United Natural Foods | CDL INVESTMENT vs. Jacquet Metal Service | CDL INVESTMENT vs. SIMS METAL MGT | CDL INVESTMENT vs. Tyson Foods |
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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