Correlation Between NYSE Composite and Dixie

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

Diversification Opportunities for NYSE Composite and Dixie

0.22
  Correlation Coefficient

Modest diversification

The 3 months correlation between NYSE and Dixie is 0.22. Overlapping area represents the amount of risk that can be diversified away by holding NYSE Composite and The Dixie Group in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Dixie Group 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 Dixie. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Dixie Group has no effect on the direction of NYSE Composite i.e., NYSE Composite and Dixie go up and down completely randomly.
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Pair Corralation between NYSE Composite and Dixie

Assuming the 90 days trading horizon NYSE Composite is expected to under-perform the Dixie. But the index apears to be less risky and, when comparing its historical volatility, NYSE Composite is 40.44 times less risky than Dixie. The index trades about -0.03 of its potential returns per unit of risk. The The Dixie Group is currently generating about 0.18 of returns per unit of risk over similar time horizon. If you would invest  70.00  in The Dixie Group on September 29, 2024 and sell it today you would earn a total of  9.00  from holding The Dixie Group or generate 12.86% return on investment over 90 days.
Time Period3 Months [change]
DirectionMoves Together 
StrengthVery Weak
Accuracy7.94%
ValuesDaily Returns

NYSE Composite  vs.  The Dixie Group

 Performance 
       Timeline  

NYSE Composite and Dixie Volatility Contrast

   Predicted Return Density   
       Returns  

Pair Trading with NYSE Composite and Dixie

The main advantage of trading using opposite NYSE Composite and Dixie positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if NYSE Composite position performs unexpectedly, Dixie 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 Dixie will offset losses from the drop in Dixie's long position.
The idea behind NYSE Composite and The Dixie Group 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 Backtesting module to avoid under-diversification and over-optimization by backtesting your portfolios.

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