Correlation Between Kosdaq Composite and OMX Helsinki

Can any of the company-specific risk be diversified away by investing in both Kosdaq Composite and OMX Helsinki 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 Kosdaq Composite and OMX Helsinki into the same portfolio, which is an essential part of the fundamental portfolio management process.

By analyzing existing cross correlation between Kosdaq Composite Index and OMX Helsinki 25, you can compare the effects of market volatilities on Kosdaq Composite and OMX Helsinki 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 Kosdaq Composite with a short position of OMX Helsinki. Check out your portfolio center. Please also check ongoing floating volatility patterns of Kosdaq Composite and OMX Helsinki.

Diversification Opportunities for Kosdaq Composite and OMX Helsinki

0.82

Correlation Coefficient

Kosdaq Composite  OMX Helsinki

Very poor diversification

The 3 months correlation between Kosdaq and OMX is 0.82. Overlapping area represents the amount of risk that can be diversified away by holding Kosdaq Composite Index and OMX Helsinki 25 in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on OMX Helsinki 25 and Kosdaq Composite 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 Kosdaq Composite Index are associated (or correlated) with OMX Helsinki. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of OMX Helsinki 25 has no effect on the direction of Kosdaq Composite i.e., Kosdaq Composite and OMX Helsinki go up and down completely randomly.

Pair Corralation between Kosdaq Composite and OMX Helsinki

Assuming the 90 days trading horizon Kosdaq Composite Index is expected to under-perform the OMX Helsinki. In addition to that, Kosdaq Composite is 1.85 times more volatile than OMX Helsinki 25. It trades about -0.13 of its total potential returns per unit of risk. OMX Helsinki 25 is currently generating about -0.15 per unit of volatility. If you would invest  469,538  in OMX Helsinki 25 on September 1, 2024 and sell it today you would lose (36,099) from holding OMX Helsinki 25 or give up 7.69% of portfolio value over 90 days.

Time Period	3 Months [change]
Direction	Moves Together
Strength	Strong
Accuracy	89.39%
Values	Daily Returns

Kosdaq Composite Index  vs.  OMX Helsinki 25

Pair Trading with Kosdaq Composite and OMX Helsinki

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

The idea behind Kosdaq Composite Index and OMX Helsinki 25 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 Pattern Recognition module to use different Pattern Recognition models to time the market across multiple global exchanges.