Correlation Between Matthews China and Abr Dynamic
Can any of the company-specific risk be diversified away by investing in both Matthews China and Abr Dynamic 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 Matthews China and Abr Dynamic into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Matthews China Small and Abr Dynamic Blend, you can compare the effects of market volatilities on Matthews China and Abr Dynamic 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 Matthews China with a short position of Abr Dynamic. Check out your portfolio center. Please also check ongoing floating volatility patterns of Matthews China and Abr Dynamic.
Diversification Opportunities for Matthews China and Abr Dynamic
0.51 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between Matthews and Abr is 0.51. Overlapping area represents the amount of risk that can be diversified away by holding Matthews China Small and Abr Dynamic Blend in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Abr Dynamic Blend and Matthews 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 Matthews China Small are associated (or correlated) with Abr Dynamic. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Abr Dynamic Blend has no effect on the direction of Matthews China i.e., Matthews China and Abr Dynamic go up and down completely randomly.
Pair Corralation between Matthews China and Abr Dynamic
Assuming the 90 days horizon Matthews China Small is expected to generate 3.83 times more return on investment than Abr Dynamic. However, Matthews China is 3.83 times more volatile than Abr Dynamic Blend. It trades about 0.06 of its potential returns per unit of risk. Abr Dynamic Blend is currently generating about 0.13 per unit of risk. If you would invest 954.00 in Matthews China Small on September 10, 2024 and sell it today you would earn a total of 17.00 from holding Matthews China Small or generate 1.78% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Weak |
Accuracy | 95.24% |
Values | Daily Returns |
Matthews China Small vs. Abr Dynamic Blend
Performance |
Timeline |
Matthews China Small |
Abr Dynamic Blend |
Matthews China and Abr Dynamic Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Matthews China and Abr Dynamic
The main advantage of trading using opposite Matthews China and Abr Dynamic positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Matthews China position performs unexpectedly, Abr Dynamic 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 Abr Dynamic will offset losses from the drop in Abr Dynamic's long position.Matthews China vs. Matthews China Dividend | Matthews China vs. Matthews Asia Innovators | Matthews China vs. Matthews Asia Small | Matthews China vs. Matthews China Fund |
Abr Dynamic vs. Rmb Mendon Financial | Abr Dynamic vs. Angel Oak Financial | Abr Dynamic vs. Goldman Sachs Financial | Abr Dynamic vs. Gabelli Global Financial |
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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