Correlation Between SOFTWARE MANSION and WIG 30

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Can any of the company-specific risk be diversified away by investing in both SOFTWARE MANSION and WIG 30 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 SOFTWARE MANSION and WIG 30 into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between SOFTWARE MANSION SPOLKA and WIG 30, you can compare the effects of market volatilities on SOFTWARE MANSION and WIG 30 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 SOFTWARE MANSION with a short position of WIG 30. Check out your portfolio center. Please also check ongoing floating volatility patterns of SOFTWARE MANSION and WIG 30.

Diversification Opportunities for SOFTWARE MANSION and WIG 30

0.15
  Correlation Coefficient

Average diversification

The 3 months correlation between SOFTWARE and WIG is 0.15. Overlapping area represents the amount of risk that can be diversified away by holding SOFTWARE MANSION SPOLKA and WIG 30 in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on WIG 30 and SOFTWARE MANSION 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 SOFTWARE MANSION SPOLKA are associated (or correlated) with WIG 30. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of WIG 30 has no effect on the direction of SOFTWARE MANSION i.e., SOFTWARE MANSION and WIG 30 go up and down completely randomly.
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Pair Corralation between SOFTWARE MANSION and WIG 30

Assuming the 90 days trading horizon SOFTWARE MANSION SPOLKA is expected to under-perform the WIG 30. In addition to that, SOFTWARE MANSION is 1.82 times more volatile than WIG 30. It trades about -0.1 of its total potential returns per unit of risk. WIG 30 is currently generating about 0.12 per unit of volatility. If you would invest  286,132  in WIG 30 on September 12, 2024 and sell it today you would earn a total of  8,335  from holding WIG 30 or generate 2.91% return on investment over 90 days.
Time Period3 Months [change]
DirectionMoves Together 
StrengthInsignificant
Accuracy95.24%
ValuesDaily Returns

SOFTWARE MANSION SPOLKA  vs.  WIG 30

 Performance 
       Timeline  

SOFTWARE MANSION and WIG 30 Volatility Contrast

   Predicted Return Density   
       Returns  

Pair Trading with SOFTWARE MANSION and WIG 30

The main advantage of trading using opposite SOFTWARE MANSION and WIG 30 positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if SOFTWARE MANSION position performs unexpectedly, WIG 30 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 WIG 30 will offset losses from the drop in WIG 30's long position.
The idea behind SOFTWARE MANSION SPOLKA and WIG 30 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 Competition Analyzer module to analyze and compare many basic indicators for a group of related or unrelated entities.

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