Correlation Between Dow Jones and Biome Grow

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

Diversification Opportunities for Dow Jones and Biome Grow

-0.23
  Correlation Coefficient

Very good diversification

The 3 months correlation between Dow and Biome is -0.23. Overlapping area represents the amount of risk that can be diversified away by holding Dow Jones Industrial and Biome Grow in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Biome Grow and Dow Jones 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 Dow Jones Industrial are associated (or correlated) with Biome Grow. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Biome Grow has no effect on the direction of Dow Jones i.e., Dow Jones and Biome Grow go up and down completely randomly.
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Pair Corralation between Dow Jones and Biome Grow

Assuming the 90 days trading horizon Dow Jones is expected to generate 69.86 times less return on investment than Biome Grow. But when comparing it to its historical volatility, Dow Jones Industrial is 53.09 times less risky than Biome Grow. It trades about 0.07 of its potential returns per unit of risk. Biome Grow is currently generating about 0.1 of returns per unit of risk over similar time horizon. If you would invest  0.83  in Biome Grow on September 29, 2024 and sell it today you would lose (0.09) from holding Biome Grow or give up 10.84% of portfolio value over 90 days.
Time Period3 Months [change]
DirectionMoves Against 
StrengthInsignificant
Accuracy99.8%
ValuesDaily Returns

Dow Jones Industrial  vs.  Biome Grow

 Performance 
       Timeline  

Dow Jones and Biome Grow Volatility Contrast

   Predicted Return Density   
       Returns  

Pair Trading with Dow Jones and Biome Grow

The main advantage of trading using opposite Dow Jones and Biome Grow positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Dow Jones position performs unexpectedly, Biome Grow 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 Biome Grow will offset losses from the drop in Biome Grow's long position.
The idea behind Dow Jones Industrial and Biome Grow 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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