Correlation Between NYSE Composite and Dfa Us

Can any of the company-specific risk be diversified away by investing in both NYSE Composite and Dfa Us 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 NYSE Composite and Dfa Us into the same portfolio, which is an essential part of the fundamental portfolio management process.

By analyzing existing cross correlation between NYSE Composite and Dfa Social Core, you can compare the effects of market volatilities on NYSE Composite and Dfa Us 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 NYSE Composite with a short position of Dfa Us. Check out your portfolio center. Please also check ongoing floating volatility patterns of NYSE Composite and Dfa Us.

Diversification Opportunities for NYSE Composite and Dfa Us

0.78

Correlation Coefficient

NYSE Composite  Dfa Us

Poor diversification

The 3 months correlation between NYSE and Dfa is 0.78. Overlapping area represents the amount of risk that can be diversified away by holding NYSE Composite and Dfa Social Core in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Dfa Social Core and NYSE Composite 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 NYSE Composite are associated (or correlated) with Dfa Us. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Dfa Social Core has no effect on the direction of NYSE Composite i.e., NYSE Composite and Dfa Us go up and down completely randomly.

Pair Corralation between NYSE Composite and Dfa Us

Assuming the 90 days trading horizon NYSE Composite is expected to under-perform the Dfa Us. But the index apears to be less risky and, when comparing its historical volatility, NYSE Composite is 1.24 times less risky than Dfa Us. The index trades about -0.07 of its potential returns per unit of risk. The Dfa Social Core is currently generating about 0.0 of returns per unit of risk over similar time horizon. If you would invest  3,005  in Dfa Social Core on October 25, 2024 and sell it today you would lose (8.00) from holding Dfa Social Core or give up 0.27% of portfolio value over 90 days.

Time Period	3 Months [change]
Direction	Moves Together
Strength	Significant
Accuracy	97.44%
Values	Daily Returns

NYSE Composite  vs.  Dfa Social Core

Pair Trading with NYSE Composite and Dfa Us

The main advantage of trading using opposite NYSE Composite and Dfa Us positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if NYSE Composite position performs unexpectedly, Dfa Us 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 Dfa Us will offset losses from the drop in Dfa Us' long position.

The idea behind NYSE Composite and Dfa Social Core 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 My Watchlist Analysis module to analyze my current watchlist and to refresh optimization strategy. Macroaxis watchlist is based on self-learning algorithm to remember stocks you like.