Correlation Between Kardan Real and Dow Jones

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

Diversification Opportunities for Kardan Real and Dow Jones

0.62
  Correlation Coefficient

Poor diversification

The 3 months correlation between Kardan and Dow is 0.62. Overlapping area represents the amount of risk that can be diversified away by holding Kardan Real Estate and Dow Jones Industrial in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Dow Jones Industrial and Kardan Real 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 Kardan Real Estate are associated (or correlated) with Dow Jones. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Dow Jones Industrial has no effect on the direction of Kardan Real i.e., Kardan Real and Dow Jones go up and down completely randomly.
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Pair Corralation between Kardan Real and Dow Jones

Assuming the 90 days trading horizon Kardan Real Estate is expected to generate 2.74 times more return on investment than Dow Jones. However, Kardan Real is 2.74 times more volatile than Dow Jones Industrial. It trades about 0.18 of its potential returns per unit of risk. Dow Jones Industrial is currently generating about 0.06 per unit of risk. If you would invest  45,980  in Kardan Real Estate on September 26, 2024 and sell it today you would earn a total of  8,680  from holding Kardan Real Estate or generate 18.88% return on investment over 90 days.
Time Period3 Months [change]
DirectionMoves Together 
StrengthSignificant
Accuracy71.43%
ValuesDaily Returns

Kardan Real Estate  vs.  Dow Jones Industrial

 Performance 
       Timeline  

Kardan Real and Dow Jones Volatility Contrast

   Predicted Return Density   
       Returns  

Pair Trading with Kardan Real and Dow Jones

The main advantage of trading using opposite Kardan Real and Dow Jones positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Kardan Real position performs unexpectedly, Dow Jones 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 Dow Jones will offset losses from the drop in Dow Jones' long position.
The idea behind Kardan Real Estate and Dow Jones Industrial 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 Watchlist Optimization module to optimize watchlists to build efficient portfolios or rebalance existing positions based on the mean-variance optimization algorithm.

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