Correlation Between Dow Jones and Christiani Nielsen
Can any of the company-specific risk be diversified away by investing in both Dow Jones and Christiani Nielsen 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 Dow Jones and Christiani Nielsen into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Dow Jones Industrial and Christiani Nielsen Public, you can compare the effects of market volatilities on Dow Jones and Christiani Nielsen 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 Dow Jones with a short position of Christiani Nielsen. Check out your portfolio center. Please also check ongoing floating volatility patterns of Dow Jones and Christiani Nielsen.
Diversification Opportunities for Dow Jones and Christiani Nielsen
-0.64 | Correlation Coefficient |
Excellent diversification
The 3 months correlation between Dow and Christiani is -0.64. Overlapping area represents the amount of risk that can be diversified away by holding Dow Jones Industrial and Christiani Nielsen Public in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Christiani Nielsen Public and Dow Jones 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 Dow Jones Industrial are associated (or correlated) with Christiani Nielsen. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Christiani Nielsen Public has no effect on the direction of Dow Jones i.e., Dow Jones and Christiani Nielsen go up and down completely randomly.
Pair Corralation between Dow Jones and Christiani Nielsen
Assuming the 90 days trading horizon Dow Jones Industrial is expected to generate 0.31 times more return on investment than Christiani Nielsen. However, Dow Jones Industrial is 3.22 times less risky than Christiani Nielsen. It trades about -0.04 of its potential returns per unit of risk. Christiani Nielsen Public is currently generating about -0.03 per unit of risk. If you would invest 4,257,373 in Dow Jones Industrial on December 28, 2024 and sell it today you would lose (98,983) from holding Dow Jones Industrial or give up 2.32% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Weak |
Accuracy | 98.39% |
Values | Daily Returns |
Dow Jones Industrial vs. Christiani Nielsen Public
Performance |
Timeline |
Dow Jones and Christiani Nielsen Volatility Contrast
Predicted Return Density |
Returns |
Dow Jones Industrial
Pair trading matchups for Dow Jones
Christiani Nielsen Public
Pair trading matchups for Christiani Nielsen
Pair Trading with Dow Jones and Christiani Nielsen
The main advantage of trading using opposite Dow Jones and Christiani Nielsen positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Dow Jones position performs unexpectedly, Christiani Nielsen 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 Christiani Nielsen will offset losses from the drop in Christiani Nielsen's long position.Dow Jones vs. PennantPark Investment | Dow Jones vs. Western Asset Investment | Dow Jones vs. Yoshitsu Co Ltd | Dow Jones vs. Black Hills |
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Check out your portfolio center.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 Pattern Recognition module to use different Pattern Recognition models to time the market across multiple global exchanges.
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