Correlation Between Zhihu and NYSE Composite

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Can any of the company-specific risk be diversified away by investing in both Zhihu and NYSE Composite 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 Zhihu and NYSE Composite into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Zhihu Inc ADR and NYSE Composite, you can compare the effects of market volatilities on Zhihu and NYSE Composite 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 Zhihu with a short position of NYSE Composite. Check out your portfolio center. Please also check ongoing floating volatility patterns of Zhihu and NYSE Composite.

Diversification Opportunities for Zhihu and NYSE Composite

-0.11
  Correlation Coefficient

Good diversification

The 3 months correlation between Zhihu and NYSE is -0.11. Overlapping area represents the amount of risk that can be diversified away by holding Zhihu Inc ADR and NYSE Composite in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on NYSE Composite and Zhihu 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 Zhihu Inc ADR are associated (or correlated) with NYSE Composite. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of NYSE Composite has no effect on the direction of Zhihu i.e., Zhihu and NYSE Composite go up and down completely randomly.
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Pair Corralation between Zhihu and NYSE Composite

Allowing for the 90-day total investment horizon Zhihu Inc ADR is expected to generate 6.41 times more return on investment than NYSE Composite. However, Zhihu is 6.41 times more volatile than NYSE Composite. It trades about 0.03 of its potential returns per unit of risk. NYSE Composite is currently generating about -0.02 per unit of risk. If you would invest  352.00  in Zhihu Inc ADR on September 26, 2024 and sell it today you would earn a total of  12.00  from holding Zhihu Inc ADR or generate 3.41% return on investment over 90 days.
Time Period3 Months [change]
DirectionMoves Against 
StrengthInsignificant
Accuracy100.0%
ValuesDaily Returns

Zhihu Inc ADR  vs.  NYSE Composite

 Performance 
       Timeline  

Zhihu and NYSE Composite Volatility Contrast

   Predicted Return Density   
       Returns  

Pair Trading with Zhihu and NYSE Composite

The main advantage of trading using opposite Zhihu and NYSE Composite positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Zhihu position performs unexpectedly, NYSE Composite 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 NYSE Composite will offset losses from the drop in NYSE Composite's long position.
The idea behind Zhihu Inc ADR and NYSE Composite pairs trading is to make the combined position market-neutral, meaning the overall market's direction will not affect its win or loss (or potential downside or upside). This can be achieved by designing a pairs trade with two highly correlated stocks or equities that operate in a similar space or sector, making it possible to obtain profits through simple and relatively low-risk investment.
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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 Center module to all portfolio management and optimization tools to improve performance of your portfolios.

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