Correlation Between NYSE Composite and Ab Ohio

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

Diversification Opportunities for NYSE Composite and Ab Ohio

0.41
  Correlation Coefficient

Very weak diversification

The 3 months correlation between NYSE and AOHAX is 0.41. Overlapping area represents the amount of risk that can be diversified away by holding NYSE Composite and Ab Ohio Portfolio in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Ab Ohio Portfolio 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 Ab Ohio. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Ab Ohio Portfolio has no effect on the direction of NYSE Composite i.e., NYSE Composite and Ab Ohio go up and down completely randomly.
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Pair Corralation between NYSE Composite and Ab Ohio

Assuming the 90 days trading horizon NYSE Composite is expected to under-perform the Ab Ohio. In addition to that, NYSE Composite is 2.76 times more volatile than Ab Ohio Portfolio. It trades about -0.28 of its total potential returns per unit of risk. Ab Ohio Portfolio is currently generating about -0.26 per unit of volatility. If you would invest  940.00  in Ab Ohio Portfolio on September 27, 2024 and sell it today you would lose (13.00) from holding Ab Ohio Portfolio or give up 1.38% of portfolio value over 90 days.
Time Period3 Months [change]
DirectionMoves Together 
StrengthWeak
Accuracy95.45%
ValuesDaily Returns

NYSE Composite  vs.  Ab Ohio Portfolio

 Performance 
       Timeline  

NYSE Composite and Ab Ohio Volatility Contrast

   Predicted Return Density   
       Returns  

Pair Trading with NYSE Composite and Ab Ohio

The main advantage of trading using opposite NYSE Composite and Ab Ohio positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if NYSE Composite position performs unexpectedly, Ab Ohio 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 Ab Ohio will offset losses from the drop in Ab Ohio's long position.
The idea behind NYSE Composite and Ab Ohio Portfolio 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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