Correlation Between NYSE Composite and Frost Total

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

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

Diversification Opportunities for NYSE Composite and Frost Total

-0.45

Correlation Coefficient

NYSE Composite  Frost Total

Very good diversification

The 3 months correlation between NYSE and Frost is -0.45. Overlapping area represents the amount of risk that can be diversified away by holding NYSE Composite and Frost Total Return in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Frost Total Return 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 Frost Total. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Frost Total Return has no effect on the direction of NYSE Composite i.e., NYSE Composite and Frost Total go up and down completely randomly.

Pair Corralation between NYSE Composite and Frost Total

Assuming the 90 days trading horizon NYSE Composite is expected to under-perform the Frost Total. In addition to that, NYSE Composite is 1.95 times more volatile than Frost Total Return. It trades about -0.05 of its total potential returns per unit of risk. Frost Total Return is currently generating about 0.02 per unit of volatility. If you would invest  976.00  in Frost Total Return on September 18, 2024 and sell it today you would earn a total of  1.00  from holding Frost Total Return or generate 0.1% return on investment over 90 days.

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

NYSE Composite  vs.  Frost Total Return

Pair Trading with NYSE Composite and Frost Total

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

The idea behind NYSE Composite and Frost Total Return 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.

Check out your portfolio center.

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.