Correlation Between Materialise and DAX Index

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

Diversification Opportunities for Materialise and DAX Index

0.63
  Correlation Coefficient

Poor diversification

The 3 months correlation between Materialise and DAX is 0.63. Overlapping area represents the amount of risk that can be diversified away by holding Materialise NV and DAX Index in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on DAX Index and Materialise 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 Materialise NV are associated (or correlated) with DAX Index. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of DAX Index has no effect on the direction of Materialise i.e., Materialise and DAX Index go up and down completely randomly.
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Pair Corralation between Materialise and DAX Index

Assuming the 90 days trading horizon Materialise NV is expected to generate 7.28 times more return on investment than DAX Index. However, Materialise is 7.28 times more volatile than DAX Index. It trades about 0.17 of its potential returns per unit of risk. DAX Index is currently generating about 0.48 per unit of risk. If you would invest  650.00  in Materialise NV on September 16, 2024 and sell it today you would earn a total of  95.00  from holding Materialise NV or generate 14.62% return on investment over 90 days.
Time Period3 Months [change]
DirectionMoves Together 
StrengthSignificant
Accuracy100.0%
ValuesDaily Returns

Materialise NV  vs.  DAX Index

 Performance 
       Timeline  

Materialise and DAX Index Volatility Contrast

   Predicted Return Density   
       Returns  

Pair Trading with Materialise and DAX Index

The main advantage of trading using opposite Materialise and DAX Index positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Materialise position performs unexpectedly, DAX Index 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 DAX Index will offset losses from the drop in DAX Index's long position.
The idea behind Materialise NV and DAX 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 My Watchlist Analysis module to analyze my current watchlist and to refresh optimization strategy. Macroaxis watchlist is based on self-learning algorithm to remember stocks you like.

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