Correlation Between GameStop Corp and NYSE Composite

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

Diversification Opportunities for GameStop Corp and NYSE Composite

  Correlation Coefficient

Very good diversification

The 3 months correlation between GameStop and NYSE is -0.29. Overlapping area represents the amount of risk that can be diversified away by holding GameStop Corp and NYSE Composite in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on NYSE Composite and GameStop Corp 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 GameStop Corp 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 GameStop Corp i.e., GameStop Corp and NYSE Composite go up and down completely randomly.

Pair Corralation between GameStop Corp and NYSE Composite

Considering the 90-day investment horizon GameStop Corp is expected to under-perform the NYSE Composite. In addition to that, GameStop Corp is 6.51 times more volatile than NYSE Composite. It trades about -0.44 of its total potential returns per unit of risk. NYSE Composite is currently generating about -0.19 per unit of volatility. If you would invest  1,807,715  in NYSE Composite on January 23, 2024 and sell it today you would lose (47,496) from holding NYSE Composite or give up 2.63% of portfolio value over 90 days.
Time Period3 Months [change]
DirectionMoves Against 
ValuesDaily Returns

GameStop Corp  vs.  NYSE Composite


GameStop Corp and NYSE Composite Volatility Contrast

   Predicted Return Density   

Pair Trading with GameStop Corp and NYSE Composite

The main advantage of trading using opposite GameStop Corp and NYSE Composite positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if GameStop Corp 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 GameStop Corp 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 Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.

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