Correlation Between NYSE Composite and First Trust

Can any of the company-specific risk be diversified away by investing in both NYSE Composite and First Trust 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 First Trust into the same portfolio, which is an essential part of the fundamental portfolio management process.

By analyzing existing cross correlation between NYSE Composite and First Trust Lunt, you can compare the effects of market volatilities on NYSE Composite and First Trust 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 First Trust. Check out your portfolio center. Please also check ongoing floating volatility patterns of NYSE Composite and First Trust.

Diversification Opportunities for NYSE Composite and First Trust

0.89

Correlation Coefficient

NYSE Composite  First Trust

Very poor diversification

The 3 months correlation between NYSE and First is 0.89. Overlapping area represents the amount of risk that can be diversified away by holding NYSE Composite and First Trust Lunt in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on First Trust Lunt 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 First Trust. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of First Trust Lunt has no effect on the direction of NYSE Composite i.e., NYSE Composite and First Trust go up and down completely randomly.

Pair Corralation between NYSE Composite and First Trust

Assuming the 90 days trading horizon NYSE Composite is expected to generate 12.1 times less return on investment than First Trust. But when comparing it to its historical volatility, NYSE Composite is 1.63 times less risky than First Trust. It trades about 0.02 of its potential returns per unit of risk. First Trust Lunt is currently generating about 0.12 of returns per unit of risk over similar time horizon. If you would invest  3,068  in First Trust Lunt on September 19, 2024 and sell it today you would earn a total of  198.00  from holding First Trust Lunt or generate 6.45% return on investment over 90 days.

Time Period	3 Months [change]
Direction	Moves Together
Strength	Strong
Accuracy	98.44%
Values	Daily Returns

NYSE Composite  vs.  First Trust Lunt

Pair Trading with NYSE Composite and First Trust

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

The idea behind NYSE Composite and First Trust Lunt 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 Equity Forecasting module to use basic forecasting models to generate price predictions and determine price momentum.