Correlation Between OMX Helsinki and Purmo Group

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Can any of the company-specific risk be diversified away by investing in both OMX Helsinki and Purmo Group 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 OMX Helsinki and Purmo Group into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between OMX Helsinki 25 and Purmo Group Oyj, you can compare the effects of market volatilities on OMX Helsinki and Purmo Group 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 OMX Helsinki with a short position of Purmo Group. Check out your portfolio center. Please also check ongoing floating volatility patterns of OMX Helsinki and Purmo Group.

Diversification Opportunities for OMX Helsinki and Purmo Group

-0.68
  Correlation Coefficient

Excellent diversification

The 3 months correlation between OMX and Purmo is -0.68. Overlapping area represents the amount of risk that can be diversified away by holding OMX Helsinki 25 and Purmo Group Oyj in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Purmo Group Oyj and OMX Helsinki 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 OMX Helsinki 25 are associated (or correlated) with Purmo Group. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Purmo Group Oyj has no effect on the direction of OMX Helsinki i.e., OMX Helsinki and Purmo Group go up and down completely randomly.
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Pair Corralation between OMX Helsinki and Purmo Group

Assuming the 90 days trading horizon OMX Helsinki 25 is expected to under-perform the Purmo Group. In addition to that, OMX Helsinki is 1.33 times more volatile than Purmo Group Oyj. It trades about -0.1 of its total potential returns per unit of risk. Purmo Group Oyj is currently generating about 0.14 per unit of volatility. If you would invest  1,130  in Purmo Group Oyj on October 7, 2024 and sell it today you would earn a total of  15.00  from holding Purmo Group Oyj or generate 1.33% return on investment over 90 days.
Time Period3 Months [change]
DirectionMoves Against 
StrengthWeak
Accuracy94.12%
ValuesDaily Returns

OMX Helsinki 25  vs.  Purmo Group Oyj

 Performance 
       Timeline  

OMX Helsinki and Purmo Group Volatility Contrast

   Predicted Return Density   
       Returns  

Pair Trading with OMX Helsinki and Purmo Group

The main advantage of trading using opposite OMX Helsinki and Purmo Group positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if OMX Helsinki position performs unexpectedly, Purmo Group 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 Purmo Group will offset losses from the drop in Purmo Group's long position.
The idea behind OMX Helsinki 25 and Purmo Group Oyj 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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