Correlation Between NYSE Composite and Franklin Exponential

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

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

Diversification Opportunities for NYSE Composite and Franklin Exponential

0.69

Correlation Coefficient

NYSE Composite  Franklin Exponential

Poor diversification

The 3 months correlation between NYSE and Franklin is 0.69. Overlapping area represents the amount of risk that can be diversified away by holding NYSE Composite and Franklin Exponential Data in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Franklin Exponential Data 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 Franklin Exponential. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Franklin Exponential Data has no effect on the direction of NYSE Composite i.e., NYSE Composite and Franklin Exponential go up and down completely randomly.

Pair Corralation between NYSE Composite and Franklin Exponential

Assuming the 90 days trading horizon NYSE Composite is expected to generate 0.53 times more return on investment than Franklin Exponential. However, NYSE Composite is 1.9 times less risky than Franklin Exponential. It trades about -0.04 of its potential returns per unit of risk. Franklin Exponential Data is currently generating about -0.12 per unit of risk. If you would invest  2,018,860  in NYSE Composite on December 4, 2024 and sell it today you would lose (36,512) from holding NYSE Composite or give up 1.81% of portfolio value over 90 days.

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

NYSE Composite  vs.  Franklin Exponential Data

Pair Trading with NYSE Composite and Franklin Exponential

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

The idea behind NYSE Composite and Franklin Exponential Data 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 Watchlist Optimization module to optimize watchlists to build efficient portfolios or rebalance existing positions based on the mean-variance optimization algorithm.