Correlation Between Mlk Foods and Warsaw WIG80

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Can any of the company-specific risk be diversified away by investing in both Mlk Foods and Warsaw WIG80 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 Mlk Foods and Warsaw WIG80 into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Mlk Foods Public and Warsaw WIG80 Index, you can compare the effects of market volatilities on Mlk Foods and Warsaw WIG80 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 Mlk Foods with a short position of Warsaw WIG80. Check out your portfolio center. Please also check ongoing floating volatility patterns of Mlk Foods and Warsaw WIG80.

Diversification Opportunities for Mlk Foods and Warsaw WIG80

-0.12
  Correlation Coefficient

Good diversification

The 3 months correlation between Mlk and Warsaw is -0.12. Overlapping area represents the amount of risk that can be diversified away by holding Mlk Foods Public and Warsaw WIG80 Index in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Warsaw WIG80 Index and Mlk Foods 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 Mlk Foods Public are associated (or correlated) with Warsaw WIG80. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Warsaw WIG80 Index has no effect on the direction of Mlk Foods i.e., Mlk Foods and Warsaw WIG80 go up and down completely randomly.
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Pair Corralation between Mlk Foods and Warsaw WIG80

Assuming the 90 days trading horizon Mlk Foods Public is expected to generate 8.7 times more return on investment than Warsaw WIG80. However, Mlk Foods is 8.7 times more volatile than Warsaw WIG80 Index. It trades about 0.06 of its potential returns per unit of risk. Warsaw WIG80 Index is currently generating about -0.15 per unit of risk. If you would invest  110.00  in Mlk Foods Public on August 29, 2024 and sell it today you would earn a total of  5.00  from holding Mlk Foods Public or generate 4.55% return on investment over 90 days.
Time Period3 Months [change]
DirectionMoves Against 
StrengthInsignificant
Accuracy100.0%
ValuesDaily Returns

Mlk Foods Public  vs.  Warsaw WIG80 Index

 Performance 
       Timeline  

Mlk Foods and Warsaw WIG80 Volatility Contrast

   Predicted Return Density   
       Returns  

Pair Trading with Mlk Foods and Warsaw WIG80

The main advantage of trading using opposite Mlk Foods and Warsaw WIG80 positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Mlk Foods position performs unexpectedly, Warsaw WIG80 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 Warsaw WIG80 will offset losses from the drop in Warsaw WIG80's long position.
The idea behind Mlk Foods Public and Warsaw WIG80 Index 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 Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.

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