Correlation Between NYSE Composite and Logility

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

Diversification Opportunities for NYSE Composite and Logility

0.08
  Correlation Coefficient

Significant diversification

The 3 months correlation between NYSE and Logility is 0.08. Overlapping area represents the amount of risk that can be diversified away by holding NYSE Composite and Logility in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Logility 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 Logility. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Logility has no effect on the direction of NYSE Composite i.e., NYSE Composite and Logility go up and down completely randomly.
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Pair Corralation between NYSE Composite and Logility

Assuming the 90 days trading horizon NYSE Composite is expected to under-perform the Logility. But the index apears to be less risky and, when comparing its historical volatility, NYSE Composite is 4.18 times less risky than Logility. The index trades about -0.03 of its potential returns per unit of risk. The Logility is currently generating about 0.02 of returns per unit of risk over similar time horizon. If you would invest  1,108  in Logility on September 29, 2024 and sell it today you would earn a total of  21.00  from holding Logility or generate 1.9% return on investment over 90 days.
Time Period3 Months [change]
DirectionMoves Together 
StrengthInsignificant
Accuracy100.0%
ValuesDaily Returns

NYSE Composite  vs.  Logility

 Performance 
       Timeline  

NYSE Composite and Logility Volatility Contrast

   Predicted Return Density   
       Returns  

Pair Trading with NYSE Composite and Logility

The main advantage of trading using opposite NYSE Composite and Logility positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if NYSE Composite position performs unexpectedly, Logility 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 Logility will offset losses from the drop in Logility's long position.
The idea behind NYSE Composite and Logility 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 Performance Analysis module to check effects of mean-variance optimization against your current asset allocation.

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