Correlation Between NYSE Composite and Swan Hedged

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

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

Diversification Opportunities for NYSE Composite and Swan Hedged

0.71

Correlation Coefficient

NYSE Composite  Swan Hedged

Poor diversification

The 3 months correlation between NYSE and Swan is 0.71. Overlapping area represents the amount of risk that can be diversified away by holding NYSE Composite and Swan Hedged Equity in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Swan Hedged Equity 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 Swan Hedged. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Swan Hedged Equity has no effect on the direction of NYSE Composite i.e., NYSE Composite and Swan Hedged go up and down completely randomly.

Pair Corralation between NYSE Composite and Swan Hedged

Assuming the 90 days trading horizon NYSE Composite is expected to under-perform the Swan Hedged. In addition to that, NYSE Composite is 1.51 times more volatile than Swan Hedged Equity. It trades about -0.36 of its total potential returns per unit of risk. Swan Hedged Equity is currently generating about -0.04 per unit of volatility. If you would invest  2,275  in Swan Hedged Equity on September 23, 2024 and sell it today you would lose (8.00) from holding Swan Hedged Equity or give up 0.35% 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.  Swan Hedged Equity

Pair Trading with NYSE Composite and Swan Hedged

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

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