Correlation Between Twitter and NYSE Composite

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

Diversification Opportunities for Twitter and NYSE Composite

0.46
  Correlation Coefficient

Very weak diversification

The 3 months correlation between Twitter and NYSE Composite is 0.46. Overlapping area represents the amount of risk that can be diversified away by holding Twitter and NYSE Composite in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on NYSE Composite and Twitter 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 Twitter 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 Twitter i.e., Twitter and NYSE Composite go up and down completely randomly.
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Pair Corralation between Twitter and NYSE Composite

Given the investment horizon of 90 days Twitter is expected to generate 3.03 times more return on investment than NYSE Composite. However, Twitter is 3.03 times more volatile than NYSE Composite. It trades about 0.01 of its potential returns per unit of risk. NYSE Composite is currently generating about 0.01 per unit of risk. If you would invest  5,820  in Twitter on October 29, 2022 and sell it today you would lose (450.00)  from holding Twitter or give up 7.73% of portfolio value over 90 days.
Time Period3 Months [change]
DirectionMoves Together 
StrengthWeak
Accuracy87.9%
ValuesDaily Returns

Twitter  vs.  NYSE Composite

 Performance (%) 
       Timeline  

Twitter and NYSE Composite Volatility Contrast

   Predicted Return Density   
       Returns  

Pair Trading with Twitter and NYSE Composite

The main advantage of trading using opposite Twitter and NYSE Composite positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Twitter 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.
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The idea behind Twitter 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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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 Portfolio Center module to all portfolio management and optimization tools to improve performance of your portfolios.

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