Correlation Between Designer Brands and NYSE Composite

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

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

Diversification Opportunities for Designer Brands and NYSE Composite

0.71

Correlation Coefficient

Designer Brands  NYSE Composite

Poor diversification

The 3 months correlation between Designer and NYSE is 0.71. Overlapping area represents the amount of risk that can be diversified away by holding Designer Brands and NYSE Composite in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on NYSE Composite and Designer Brands 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 Designer Brands 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 Designer Brands i.e., Designer Brands and NYSE Composite go up and down completely randomly.

Pair Corralation between Designer Brands and NYSE Composite

Considering the 90-day investment horizon Designer Brands is expected to under-perform the NYSE Composite. In addition to that, Designer Brands is 5.81 times more volatile than NYSE Composite. It trades about -0.13 of its total potential returns per unit of risk. NYSE Composite is currently generating about -0.18 per unit of volatility. If you would invest  1,821,619  in NYSE Composite on January 30, 2024 and sell it today you would lose (45,292) from holding NYSE Composite or give up 2.49% of portfolio value over 90 days.

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

Designer Brands  vs.  NYSE Composite

Pair Trading with Designer Brands and NYSE Composite

The main advantage of trading using opposite Designer Brands and NYSE Composite positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Designer Brands 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 Designer Brands 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 Watchlist Optimization module to optimize watchlists to build efficient portfolios or rebalance existing positions based on the mean-variance optimization algorithm.