Correlation Between ETF Series and Dow Jones

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

Diversification Opportunities for ETF Series and Dow Jones

0.8
  Correlation Coefficient

Very poor diversification

The 3 months correlation between ETF and Dow is 0.8. Overlapping area represents the amount of risk that can be diversified away by holding ETF Series Solutions 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 ETF Series 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 ETF Series Solutions 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 ETF Series i.e., ETF Series and Dow Jones go up and down completely randomly.
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Pair Corralation between ETF Series and Dow Jones

Given the investment horizon of 90 days ETF Series Solutions is expected to generate 1.46 times more return on investment than Dow Jones. However, ETF Series is 1.46 times more volatile than Dow Jones Industrial. It trades about 0.04 of its potential returns per unit of risk. Dow Jones Industrial is currently generating about 0.02 per unit of risk. If you would invest  3,252  in ETF Series Solutions on August 6, 2024 and sell it today you would earn a total of  21.00  from holding ETF Series Solutions or generate 0.65% return on investment over 90 days.
Time Period3 Months [change]
DirectionMoves Together 
StrengthStrong
Accuracy100.0%
ValuesDaily Returns

ETF Series Solutions  vs.  Dow Jones Industrial

 Performance 
       Timeline  

ETF Series and Dow Jones Volatility Contrast

   Predicted Return Density   
       Returns  

Pair Trading with ETF Series and Dow Jones

The main advantage of trading using opposite ETF Series and Dow Jones positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if ETF Series 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 ETF Series Solutions 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 Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.

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