Correlation Between LT Foods and Dow Jones

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

Diversification Opportunities for LT Foods and Dow Jones

-0.38
  Correlation Coefficient

Very good diversification

The 3 months correlation between LTFOODS and Dow is -0.38. Overlapping area represents the amount of risk that can be diversified away by holding LT Foods Limited 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 LT Foods 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 LT Foods Limited 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 LT Foods i.e., LT Foods and Dow Jones go up and down completely randomly.
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Pair Corralation between LT Foods and Dow Jones

Assuming the 90 days trading horizon LT Foods is expected to generate 2.16 times less return on investment than Dow Jones. In addition to that, LT Foods is 3.96 times more volatile than Dow Jones Industrial. It trades about 0.02 of its total potential returns per unit of risk. Dow Jones Industrial is currently generating about 0.19 per unit of volatility. If you would invest  4,097,497  in Dow Jones Industrial on September 4, 2024 and sell it today you would earn a total of  380,703  from holding Dow Jones Industrial or generate 9.29% return on investment over 90 days.
Time Period3 Months [change]
DirectionMoves Against 
StrengthInsignificant
Accuracy100.0%
ValuesDaily Returns

LT Foods Limited  vs.  Dow Jones Industrial

 Performance 
       Timeline  

LT Foods and Dow Jones Volatility Contrast

   Predicted Return Density   
       Returns  

Pair Trading with LT Foods and Dow Jones

The main advantage of trading using opposite LT Foods and Dow Jones positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if LT Foods 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 LT Foods Limited 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 Headlines Timeline module to stay connected to all market stories and filter out noise. Drill down to analyze hype elasticity.

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