Correlation Between Cboe UK and Solid State

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Can any of the company-specific risk be diversified away by investing in both Cboe UK and Solid State 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 Cboe UK and Solid State into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Cboe UK Consumer and Solid State Plc, you can compare the effects of market volatilities on Cboe UK and Solid State 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 Cboe UK with a short position of Solid State. Check out your portfolio center. Please also check ongoing floating volatility patterns of Cboe UK and Solid State.

Diversification Opportunities for Cboe UK and Solid State

-0.94
  Correlation Coefficient

Pay attention - limited upside

The 3 months correlation between Cboe and Solid is -0.94. Overlapping area represents the amount of risk that can be diversified away by holding Cboe UK Consumer and Solid State Plc in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Solid State Plc and Cboe UK 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 Cboe UK Consumer are associated (or correlated) with Solid State. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Solid State Plc has no effect on the direction of Cboe UK i.e., Cboe UK and Solid State go up and down completely randomly.
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Pair Corralation between Cboe UK and Solid State

Assuming the 90 days trading horizon Cboe UK Consumer is expected to generate 0.16 times more return on investment than Solid State. However, Cboe UK Consumer is 6.15 times less risky than Solid State. It trades about 0.2 of its potential returns per unit of risk. Solid State Plc is currently generating about -0.14 per unit of risk. If you would invest  2,887,563  in Cboe UK Consumer on September 19, 2024 and sell it today you would earn a total of  362,566  from holding Cboe UK Consumer or generate 12.56% return on investment over 90 days.
Time Period3 Months [change]
DirectionMoves Against 
StrengthSignificant
Accuracy100.0%
ValuesDaily Returns

Cboe UK Consumer  vs.  Solid State Plc

 Performance 
       Timeline  

Cboe UK and Solid State Volatility Contrast

   Predicted Return Density   
       Returns  

Pair Trading with Cboe UK and Solid State

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

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