Correlation Between Morgan Stanley and FS Credit
Can any of the company-specific risk be diversified away by investing in both Morgan Stanley and FS Credit 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 FS Credit into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Morgan Stanley and FS Credit Opportunities, you can compare the effects of market volatilities on Morgan Stanley and FS Credit 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 FS Credit. Check out your portfolio center. Please also check ongoing floating volatility patterns of Morgan Stanley and FS Credit.
Diversification Opportunities for Morgan Stanley and FS Credit
-0.26 | Correlation Coefficient |
Very good diversification
The 3 months correlation between Morgan and FSCO is -0.26. Overlapping area represents the amount of risk that can be diversified away by holding Morgan Stanley and FS Credit Opportunities in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on FS Credit Opportunities 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 are associated (or correlated) with FS Credit. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of FS Credit Opportunities has no effect on the direction of Morgan Stanley i.e., Morgan Stanley and FS Credit go up and down completely randomly.
Pair Corralation between Morgan Stanley and FS Credit
Allowing for the 90-day total investment horizon Morgan Stanley is expected to under-perform the FS Credit. In addition to that, Morgan Stanley is 2.45 times more volatile than FS Credit Opportunities. It trades about -0.01 of its total potential returns per unit of risk. FS Credit Opportunities is currently generating about 0.16 per unit of volatility. If you would invest 649.00 in FS Credit Opportunities on December 27, 2024 and sell it today you would earn a total of 55.00 from holding FS Credit Opportunities or generate 8.47% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
Morgan Stanley vs. FS Credit Opportunities
Performance |
Timeline |
Morgan Stanley |
FS Credit Opportunities |
Morgan Stanley and FS Credit Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Morgan Stanley and FS Credit
The main advantage of trading using opposite Morgan Stanley and FS Credit positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Morgan Stanley position performs unexpectedly, FS Credit 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 FS Credit will offset losses from the drop in FS Credit's long position.Morgan Stanley vs. Goldman Sachs Group | Morgan Stanley vs. Riot Blockchain | Morgan Stanley vs. Marathon Digital Holdings | Morgan Stanley vs. Applied Digital |
FS Credit vs. MFS Investment Grade | FS Credit vs. Eaton Vance National | FS Credit vs. Federated Premier Municipal | FS Credit vs. Investcorp Credit Management |
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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