Correlation Between Dow Jones and Wulandari Bangun
Can any of the company-specific risk be diversified away by investing in both Dow Jones and Wulandari Bangun 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 Wulandari Bangun 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 Wulandari Bangun Laksana, you can compare the effects of market volatilities on Dow Jones and Wulandari Bangun 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 Wulandari Bangun. Check out your portfolio center. Please also check ongoing floating volatility patterns of Dow Jones and Wulandari Bangun.
Diversification Opportunities for Dow Jones and Wulandari Bangun
-0.72 | Correlation Coefficient |
Pay attention - limited upside
The 3 months correlation between Dow and Wulandari is -0.72. Overlapping area represents the amount of risk that can be diversified away by holding Dow Jones Industrial and Wulandari Bangun Laksana in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Wulandari Bangun Laksana 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 Wulandari Bangun. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Wulandari Bangun Laksana has no effect on the direction of Dow Jones i.e., Dow Jones and Wulandari Bangun go up and down completely randomly.
Pair Corralation between Dow Jones and Wulandari Bangun
Assuming the 90 days trading horizon Dow Jones Industrial is expected to generate 0.36 times more return on investment than Wulandari Bangun. However, Dow Jones Industrial is 2.75 times less risky than Wulandari Bangun. It trades about 0.04 of its potential returns per unit of risk. Wulandari Bangun Laksana is currently generating about -0.15 per unit of risk. If you would invest 4,215,697 in Dow Jones Industrial on October 1, 2024 and sell it today you would earn a total of 83,524 from holding Dow Jones Industrial or generate 1.98% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Weak |
Accuracy | 96.88% |
Values | Daily Returns |
Dow Jones Industrial vs. Wulandari Bangun Laksana
Performance |
Timeline |
Dow Jones and Wulandari Bangun Volatility Contrast
Predicted Return Density |
Returns |
Dow Jones Industrial
Pair trading matchups for Dow Jones
Wulandari Bangun Laksana
Pair trading matchups for Wulandari Bangun
Pair Trading with Dow Jones and Wulandari Bangun
The main advantage of trading using opposite Dow Jones and Wulandari Bangun positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Dow Jones position performs unexpectedly, Wulandari Bangun 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 Wulandari Bangun will offset losses from the drop in Wulandari Bangun's long position.Dow Jones vs. Elmos Semiconductor SE | Dow Jones vs. Lindblad Expeditions Holdings | Dow Jones vs. Arm Holdings plc | Dow Jones vs. JD Sports Fashion |
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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 Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.
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