Correlation Between 191216DJ6 and Dow Jones

Can any of the company-specific risk be diversified away by investing in both 191216DJ6 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 191216DJ6 and Dow Jones into the same portfolio, which is an essential part of the fundamental portfolio management process.

By analyzing existing cross correlation between COCA COLA CO and Dow Jones Industrial, you can compare the effects of market volatilities on 191216DJ6 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 191216DJ6 with a short position of Dow Jones. Check out your portfolio center. Please also check ongoing floating volatility patterns of 191216DJ6 and Dow Jones.

Diversification Opportunities for 191216DJ6 and Dow Jones

-0.15

Correlation Coefficient

191216DJ6  Dow Jones

Good diversification

The 3 months correlation between 191216DJ6 and Dow is -0.15. Overlapping area represents the amount of risk that can be diversified away by holding COCA COLA CO 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 191216DJ6 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 COCA COLA CO 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 191216DJ6 i.e., 191216DJ6 and Dow Jones go up and down completely randomly.

Pair Corralation between 191216DJ6 and Dow Jones

Assuming the 90 days trading horizon 191216DJ6 is expected to generate 8.42 times less return on investment than Dow Jones. But when comparing it to its historical volatility, COCA COLA CO is 1.45 times less risky than Dow Jones. It trades about 0.01 of its potential returns per unit of risk. Dow Jones Industrial is currently generating about 0.08 of returns per unit of risk over similar time horizon. If you would invest  3,326,977  in Dow Jones Industrial on September 25, 2024 and sell it today you would earn a total of  963,718  from holding Dow Jones Industrial or generate 28.97% return on investment over 90 days.

Time Period	3 Months [change]
Direction	Moves Against
Strength	Insignificant
Accuracy	94.76%
Values	Daily Returns

COCA COLA CO  vs.  Dow Jones Industrial

Pair Trading with 191216DJ6 and Dow Jones

The main advantage of trading using opposite 191216DJ6 and Dow Jones positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if 191216DJ6 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 COCA COLA CO 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.