Correlation Between Matthews China and Cargile Fund
Can any of the company-specific risk be diversified away by investing in both Matthews China and Cargile Fund 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 Cargile Fund into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Matthews China Fund and Cargile Fund, you can compare the effects of market volatilities on Matthews China and Cargile Fund 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 Cargile Fund. Check out your portfolio center. Please also check ongoing floating volatility patterns of Matthews China and Cargile Fund.
Diversification Opportunities for Matthews China and Cargile Fund
0.21 | Correlation Coefficient |
Modest diversification
The 3 months correlation between Matthews and Cargile is 0.21. Overlapping area represents the amount of risk that can be diversified away by holding Matthews China Fund and Cargile Fund in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Cargile Fund 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 Fund are associated (or correlated) with Cargile Fund. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Cargile Fund has no effect on the direction of Matthews China i.e., Matthews China and Cargile Fund go up and down completely randomly.
Pair Corralation between Matthews China and Cargile Fund
Assuming the 90 days horizon Matthews China Fund is expected to under-perform the Cargile Fund. In addition to that, Matthews China is 1.38 times more volatile than Cargile Fund. It trades about -0.49 of its total potential returns per unit of risk. Cargile Fund is currently generating about -0.22 per unit of volatility. If you would invest 916.00 in Cargile Fund on October 12, 2024 and sell it today you would lose (23.00) from holding Cargile Fund or give up 2.51% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Very Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Matthews China Fund vs. Cargile Fund
Performance |
Timeline |
Matthews China |
Cargile Fund |
Matthews China and Cargile Fund Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Matthews China and Cargile Fund
The main advantage of trading using opposite Matthews China and Cargile Fund positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Matthews China position performs unexpectedly, Cargile Fund 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 Cargile Fund will offset losses from the drop in Cargile Fund's long position.Matthews China vs. Matthews India Fund | Matthews China vs. Matthews Pacific Tiger | Matthews China vs. Matthews Asian Growth | Matthews China vs. Guinness Atkinson China |
Cargile Fund vs. Fulcrum Diversified Absolute | Cargile Fund vs. Tiaa Cref Small Cap Blend | Cargile Fund vs. Wells Fargo Diversified | Cargile Fund vs. Madison Diversified Income |
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 Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.
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