Correlation Between Federal Realty and Cboe UK

Can any of the company-specific risk be diversified away by investing in both Federal Realty and Cboe UK 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 Federal Realty and Cboe UK into the same portfolio, which is an essential part of the fundamental portfolio management process.

By analyzing existing cross correlation between Federal Realty Investment and Cboe UK Consumer, you can compare the effects of market volatilities on Federal Realty and Cboe UK 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 Federal Realty with a short position of Cboe UK. Check out your portfolio center. Please also check ongoing floating volatility patterns of Federal Realty and Cboe UK.

Diversification Opportunities for Federal Realty and Cboe UK

0.47

Correlation Coefficient

Federal Realty  Cboe UK

Very weak diversification

The 3 months correlation between Federal and Cboe is 0.47. Overlapping area represents the amount of risk that can be diversified away by holding Federal Realty Investment and Cboe UK Consumer in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Cboe UK Consumer and Federal Realty 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 Federal Realty Investment are associated (or correlated) with Cboe UK. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Cboe UK Consumer has no effect on the direction of Federal Realty i.e., Federal Realty and Cboe UK go up and down completely randomly.

Pair Corralation between Federal Realty and Cboe UK

Assuming the 90 days trading horizon Federal Realty Investment is expected to under-perform the Cboe UK. In addition to that, Federal Realty is 1.02 times more volatile than Cboe UK Consumer. It trades about -0.02 of its total potential returns per unit of risk. Cboe UK Consumer is currently generating about 0.18 per unit of volatility. If you would invest  2,879,235  in Cboe UK Consumer on September 30, 2024 and sell it today you would earn a total of  309,514  from holding Cboe UK Consumer or generate 10.75% return on investment over 90 days.

Time Period	3 Months [change]
Direction	Moves Together
Strength	Weak
Accuracy	98.44%
Values	Daily Returns

Federal Realty Investment  vs.  Cboe UK Consumer

Pair Trading with Federal Realty and Cboe UK

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

The idea behind Federal Realty Investment and Cboe UK Consumer 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.