Correlation Between FT Cboe and Matthews China
Can any of the company-specific risk be diversified away by investing in both FT Cboe and Matthews China 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 FT Cboe and Matthews China into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between FT Cboe Vest and Matthews China Discovery, you can compare the effects of market volatilities on FT Cboe and Matthews China 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 FT Cboe with a short position of Matthews China. Check out your portfolio center. Please also check ongoing floating volatility patterns of FT Cboe and Matthews China.
Diversification Opportunities for FT Cboe and Matthews China
0.55 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between DJUL and Matthews is 0.55. Overlapping area represents the amount of risk that can be diversified away by holding FT Cboe Vest and Matthews China Discovery in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Matthews China Discovery and FT Cboe 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 FT Cboe Vest are associated (or correlated) with Matthews China. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Matthews China Discovery has no effect on the direction of FT Cboe i.e., FT Cboe and Matthews China go up and down completely randomly.
Pair Corralation between FT Cboe and Matthews China
Given the investment horizon of 90 days FT Cboe Vest is expected to generate 0.19 times more return on investment than Matthews China. However, FT Cboe Vest is 5.34 times less risky than Matthews China. It trades about 0.42 of its potential returns per unit of risk. Matthews China Discovery is currently generating about -0.04 per unit of risk. If you would invest 4,109 in FT Cboe Vest on September 1, 2024 and sell it today you would earn a total of 131.00 from holding FT Cboe Vest or generate 3.19% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
FT Cboe Vest vs. Matthews China Discovery
Performance |
| Timeline |
| FT Cboe Vest |
| Matthews China Discovery |
FT Cboe and Matthews China Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with FT Cboe and Matthews China
The main advantage of trading using opposite FT Cboe and Matthews China positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if FT Cboe position performs unexpectedly, Matthews China 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 Matthews China will offset losses from the drop in Matthews China's long position.| FT Cboe vs. Innovator ETFs Trust | FT Cboe vs. First Trust Cboe | FT Cboe vs. Innovator SP 500 | FT Cboe vs. Innovator Equity Power |
| Matthews China vs. FT Vest Equity | Matthews China vs. Northern Lights | Matthews China vs. Dimensional International High | Matthews China vs. Davis Select International |
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 Headlines Timeline module to stay connected to all market stories and filter out noise. Drill down to analyze hype elasticity.
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