Correlation Between Basic Materials and NYSE Composite

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

By analyzing existing cross correlation between Basic Materials and NYSE Composite, you can compare the effects of market volatilities on Basic Materials 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 Basic Materials with a short position of NYSE Composite. Check out your portfolio center. Please also check ongoing floating volatility patterns of Basic Materials and NYSE Composite.

Diversification Opportunities for Basic Materials and NYSE Composite

0.44

Correlation Coefficient

Basic Materials  NYSE Composite

Very weak diversification

The 3 months correlation between Basic and NYSE is 0.44. Overlapping area represents the amount of risk that can be diversified away by holding Basic Materials and NYSE Composite in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on NYSE Composite and Basic Materials 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 Basic Materials 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 Basic Materials i.e., Basic Materials and NYSE Composite go up and down completely randomly.

Pair Corralation between Basic Materials and NYSE Composite

Assuming the 90 days trading horizon Basic Materials is expected to generate 1.38 times less return on investment than NYSE Composite. In addition to that, Basic Materials is 1.96 times more volatile than NYSE Composite. It trades about 0.05 of its total potential returns per unit of risk. NYSE Composite is currently generating about 0.12 per unit of volatility. If you would invest  1,929,223  in NYSE Composite on August 30, 2024 and sell it today you would earn a total of  91,759  from holding NYSE Composite or generate 4.76% return on investment over 90 days.

Time Period	3 Months [change]
Direction	Moves Together
Strength	Weak
Accuracy	100.0%
Values	Daily Returns

Basic Materials  vs.  NYSE Composite

Pair Trading with Basic Materials and NYSE Composite

The main advantage of trading using opposite Basic Materials and NYSE Composite positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Basic Materials 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 Basic Materials 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 Performance Analysis module to check effects of mean-variance optimization against your current asset allocation.