Correlation Between Dow Jones and Yang Ming
Can any of the company-specific risk be diversified away by investing in both Dow Jones and Yang Ming 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 Yang Ming 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 Yang Ming Marine, you can compare the effects of market volatilities on Dow Jones and Yang Ming 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 Yang Ming. Check out your portfolio center. Please also check ongoing floating volatility patterns of Dow Jones and Yang Ming.
Diversification Opportunities for Dow Jones and Yang Ming
Poor diversification
The 3 months correlation between Dow and Yang is 0.75. Overlapping area represents the amount of risk that can be diversified away by holding Dow Jones Industrial and Yang Ming Marine in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Yang Ming Marine 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 Yang Ming. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Yang Ming Marine has no effect on the direction of Dow Jones i.e., Dow Jones and Yang Ming go up and down completely randomly.
Pair Corralation between Dow Jones and Yang Ming
Assuming the 90 days trading horizon Dow Jones is expected to generate 2.21 times less return on investment than Yang Ming. But when comparing it to its historical volatility, Dow Jones Industrial is 4.05 times less risky than Yang Ming. It trades about 0.12 of its potential returns per unit of risk. Yang Ming Marine is currently generating about 0.06 of returns per unit of risk over similar time horizon. If you would invest 4,453 in Yang Ming Marine on August 31, 2024 and sell it today you would earn a total of 2,867 from holding Yang Ming Marine or generate 64.38% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Significant |
| Accuracy | 98.13% |
| Values | Daily Returns |
Dow Jones Industrial vs. Yang Ming Marine
Performance |
| Timeline |
Dow Jones and Yang Ming Volatility Contrast
Predicted Return Density |
| Returns |
Dow Jones Industrial
Pair trading matchups for Dow Jones
Yang Ming Marine
Pair trading matchups for Yang Ming
Pair Trading with Dow Jones and Yang Ming
The main advantage of trading using opposite Dow Jones and Yang Ming positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Dow Jones position performs unexpectedly, Yang Ming 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 Yang Ming will offset losses from the drop in Yang Ming's long position.| Dow Jones vs. Aerofoam Metals | Dow Jones vs. ACG Metals Limited | Dow Jones vs. China Clean Energy | Dow Jones vs. Fast Retailing Co |
| Yang Ming vs. Evergreen Marine Corp | Yang Ming vs. Wan Hai Lines | Yang Ming vs. China Airlines | Yang Ming vs. Eva Airways Corp |
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 Watchlist Optimization module to optimize watchlists to build efficient portfolios or rebalance existing positions based on the mean-variance optimization algorithm.
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