Correlation Between Morgan Stanley and Jinli Group
Can any of the company-specific risk be diversified away by investing in both Morgan Stanley and Jinli Group 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 Morgan Stanley and Jinli Group into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Morgan Stanley Direct and Jinli Group Holdings, you can compare the effects of market volatilities on Morgan Stanley and Jinli Group 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 Morgan Stanley with a short position of Jinli Group. Check out your portfolio center. Please also check ongoing floating volatility patterns of Morgan Stanley and Jinli Group.
Diversification Opportunities for Morgan Stanley and Jinli Group
0.84 | Correlation Coefficient |
Very poor diversification
The 3 months correlation between Morgan and Jinli is 0.84. Overlapping area represents the amount of risk that can be diversified away by holding Morgan Stanley Direct and Jinli Group Holdings in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Jinli Group Holdings and Morgan Stanley 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 Morgan Stanley Direct are associated (or correlated) with Jinli Group. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Jinli Group Holdings has no effect on the direction of Morgan Stanley i.e., Morgan Stanley and Jinli Group go up and down completely randomly.
Pair Corralation between Morgan Stanley and Jinli Group
Given the investment horizon of 90 days Morgan Stanley is expected to generate 3.13 times less return on investment than Jinli Group. But when comparing it to its historical volatility, Morgan Stanley Direct is 3.04 times less risky than Jinli Group. It trades about 0.16 of its potential returns per unit of risk. Jinli Group Holdings is currently generating about 0.16 of returns per unit of risk over similar time horizon. If you would invest 1,130 in Jinli Group Holdings on September 17, 2024 and sell it today you would earn a total of 110.00 from holding Jinli Group Holdings or generate 9.73% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Strong |
Accuracy | 95.24% |
Values | Daily Returns |
Morgan Stanley Direct vs. Jinli Group Holdings
Performance |
Timeline |
Morgan Stanley Direct |
Jinli Group Holdings |
Morgan Stanley and Jinli Group Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Morgan Stanley and Jinli Group
The main advantage of trading using opposite Morgan Stanley and Jinli Group positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Morgan Stanley position performs unexpectedly, Jinli Group 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 Jinli Group will offset losses from the drop in Jinli Group's long position.Morgan Stanley vs. Griffon | Morgan Stanley vs. First Ship Lease | Morgan Stanley vs. HE Equipment Services | Morgan Stanley vs. Highway Holdings Limited |
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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 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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