Correlation Between NYSE Composite and China Minsh
Can any of the company-specific risk be diversified away by investing in both NYSE Composite and China Minsh 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 NYSE Composite and China Minsh into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between NYSE Composite and China Minsh, you can compare the effects of market volatilities on NYSE Composite and China Minsh 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 NYSE Composite with a short position of China Minsh. Check out your portfolio center. Please also check ongoing floating volatility patterns of NYSE Composite and China Minsh.
Diversification Opportunities for NYSE Composite and China Minsh
0.28 | Correlation Coefficient |
Modest diversification
The 3 months correlation between NYSE and China is 0.28. Overlapping area represents the amount of risk that can be diversified away by holding NYSE Composite and China Minsh in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on China Minsh and NYSE Composite 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 NYSE Composite are associated (or correlated) with China Minsh. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of China Minsh has no effect on the direction of NYSE Composite i.e., NYSE Composite and China Minsh go up and down completely randomly.
Pair Corralation between NYSE Composite and China Minsh
Assuming the 90 days trading horizon NYSE Composite is expected to generate 1.83 times more return on investment than China Minsh. However, NYSE Composite is 1.83 times more volatile than China Minsh. It trades about -0.02 of its potential returns per unit of risk. China Minsh is currently generating about -0.18 per unit of risk. If you would invest 1,998,000 in NYSE Composite on November 28, 2024 and sell it today you would lose (5,595) from holding NYSE Composite or give up 0.28% of portfolio value over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Very Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
NYSE Composite vs. China Minsh
Performance |
| Timeline |
NYSE Composite and China Minsh Volatility Contrast
Predicted Return Density |
| Returns |
NYSE Composite
Pair trading matchups for NYSE Composite
China Minsh
Pair trading matchups for China Minsh
Pair Trading with NYSE Composite and China Minsh
The main advantage of trading using opposite NYSE Composite and China Minsh positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if NYSE Composite position performs unexpectedly, China Minsh 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 China Minsh will offset losses from the drop in China Minsh's long position.| NYSE Composite vs. Inter Parfums | NYSE Composite vs. Amkor Technology | NYSE Composite vs. Unilever PLC ADR | NYSE Composite vs. Estee Lauder Companies |
| China Minsh vs. Hancock Whitney Corp | China Minsh vs. First Hawaiian | China Minsh vs. DBS Group Holdings | China Minsh vs. United Overseas Bank |
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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