Correlation Between Morgan Stanley and SPDR Series
Can any of the company-specific risk be diversified away by investing in both Morgan Stanley and SPDR Series 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 SPDR Series 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 SPDR Series Trust, you can compare the effects of market volatilities on Morgan Stanley and SPDR Series 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 SPDR Series. Check out your portfolio center. Please also check ongoing floating volatility patterns of Morgan Stanley and SPDR Series.
Diversification Opportunities for Morgan Stanley and SPDR Series
0.74 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Morgan and SPDR is 0.74. Overlapping area represents the amount of risk that can be diversified away by holding Morgan Stanley Direct and SPDR Series Trust in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on SPDR Series Trust 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 SPDR Series. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of SPDR Series Trust has no effect on the direction of Morgan Stanley i.e., Morgan Stanley and SPDR Series go up and down completely randomly.
Pair Corralation between Morgan Stanley and SPDR Series
Given the investment horizon of 90 days Morgan Stanley is expected to generate 3.45 times less return on investment than SPDR Series. But when comparing it to its historical volatility, Morgan Stanley Direct is 1.36 times less risky than SPDR Series. It trades about 0.03 of its potential returns per unit of risk. SPDR Series Trust is currently generating about 0.09 of returns per unit of risk over similar time horizon. If you would invest 335,425 in SPDR Series Trust on September 28, 2024 and sell it today you would earn a total of 186,200 from holding SPDR Series Trust or generate 55.51% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 87.97% |
Values | Daily Returns |
Morgan Stanley Direct vs. SPDR Series Trust
Performance |
Timeline |
Morgan Stanley Direct |
SPDR Series Trust |
Morgan Stanley and SPDR Series Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Morgan Stanley and SPDR Series
The main advantage of trading using opposite Morgan Stanley and SPDR Series positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Morgan Stanley position performs unexpectedly, SPDR Series 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 SPDR Series will offset losses from the drop in SPDR Series' long position.Morgan Stanley vs. Reservoir Media | Morgan Stanley vs. Grupo Simec SAB | Morgan Stanley vs. Arrow Electronics | Morgan Stanley vs. Huadi International Group |
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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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