Correlation Between Aluminumof China and Vastned Retail
Can any of the company-specific risk be diversified away by investing in both Aluminumof China and Vastned Retail 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 Aluminumof China and Vastned Retail into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Aluminum of and Vastned Retail NV, you can compare the effects of market volatilities on Aluminumof China and Vastned Retail 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 Aluminumof China with a short position of Vastned Retail. Check out your portfolio center. Please also check ongoing floating volatility patterns of Aluminumof China and Vastned Retail.
Diversification Opportunities for Aluminumof China and Vastned Retail
0.26 | Correlation Coefficient |
Modest diversification
The 3 months correlation between Aluminumof and Vastned is 0.26. Overlapping area represents the amount of risk that can be diversified away by holding Aluminum of and Vastned Retail NV in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Vastned Retail NV and Aluminumof China 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 Aluminum of are associated (or correlated) with Vastned Retail. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Vastned Retail NV has no effect on the direction of Aluminumof China i.e., Aluminumof China and Vastned Retail go up and down completely randomly.
Pair Corralation between Aluminumof China and Vastned Retail
Assuming the 90 days horizon Aluminum of is expected to generate 3.03 times more return on investment than Vastned Retail. However, Aluminumof China is 3.03 times more volatile than Vastned Retail NV. It trades about 0.08 of its potential returns per unit of risk. Vastned Retail NV is currently generating about 0.07 per unit of risk. If you would invest 31.00 in Aluminum of on October 6, 2024 and sell it today you would earn a total of 23.00 from holding Aluminum of or generate 74.19% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Very Weak |
Accuracy | 99.6% |
Values | Daily Returns |
Aluminum of vs. Vastned Retail NV
Performance |
Timeline |
Aluminumof China |
Vastned Retail NV |
Risk-Adjusted Performance
0 of 100
Weak | Strong |
Very Weak
Aluminumof China and Vastned Retail Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Aluminumof China and Vastned Retail
The main advantage of trading using opposite Aluminumof China and Vastned Retail positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Aluminumof China position performs unexpectedly, Vastned Retail 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 Vastned Retail will offset losses from the drop in Vastned Retail's long position.Aluminumof China vs. Hyrican Informationssysteme Aktiengesellschaft | Aluminumof China vs. Linedata Services SA | Aluminumof China vs. Pure Storage | Aluminumof China vs. DATATEC LTD 2 |
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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 Idea Optimizer module to use advanced portfolio builder with pre-computed micro ideas to build optimal portfolio .
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