Correlation Between Matthews China and EA Series
Can any of the company-specific risk be diversified away by investing in both Matthews China and EA Series 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 EA Series into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Matthews China Discovery and EA Series Trust, you can compare the effects of market volatilities on Matthews China and EA Series 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 EA Series. Check out your portfolio center. Please also check ongoing floating volatility patterns of Matthews China and EA Series.
Diversification Opportunities for Matthews China and EA Series
0.87 | Correlation Coefficient |
Very poor diversification
The 3 months correlation between Matthews and MDLV is 0.87. Overlapping area represents the amount of risk that can be diversified away by holding Matthews China Discovery and EA Series Trust in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on EA Series Trust 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 Discovery are associated (or correlated) with EA Series. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of EA Series Trust has no effect on the direction of Matthews China i.e., Matthews China and EA Series go up and down completely randomly.
Pair Corralation between Matthews China and EA Series
Given the investment horizon of 90 days Matthews China Discovery is expected to generate 2.07 times more return on investment than EA Series. However, Matthews China is 2.07 times more volatile than EA Series Trust. It trades about 0.11 of its potential returns per unit of risk. EA Series Trust is currently generating about 0.12 per unit of risk. If you would invest 2,574 in Matthews China Discovery on December 28, 2024 and sell it today you would earn a total of 243.00 from holding Matthews China Discovery or generate 9.44% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Strong |
Accuracy | 100.0% |
Values | Daily Returns |
Matthews China Discovery vs. EA Series Trust
Performance |
Timeline |
Matthews China Discovery |
EA Series Trust |
Matthews China and EA Series Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Matthews China and EA Series
The main advantage of trading using opposite Matthews China and EA Series positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Matthews China position performs unexpectedly, EA Series 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 EA Series will offset losses from the drop in EA Series' long position.Matthews China vs. Matthews Emerging Markets | Matthews China vs. Morgan Stanley Pathway | Matthews China vs. Neuberger Berman ETF | Matthews China vs. Fidelity Small Mid Cap |
EA Series vs. FT Vest Equity | EA Series vs. Northern Lights | EA Series vs. Dimensional International High | EA Series vs. First Trust Exchange Traded |
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 Competition Analyzer module to analyze and compare many basic indicators for a group of related or unrelated entities.
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