Correlation Between Microsoft and Shenkman Floating
Can any of the company-specific risk be diversified away by investing in both Microsoft and Shenkman Floating 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 Microsoft and Shenkman Floating into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Microsoft and Shenkman Floating Rate, you can compare the effects of market volatilities on Microsoft and Shenkman Floating 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 Microsoft with a short position of Shenkman Floating. Check out your portfolio center. Please also check ongoing floating volatility patterns of Microsoft and Shenkman Floating.
Diversification Opportunities for Microsoft and Shenkman Floating
0.06 | Correlation Coefficient |
Significant diversification
The 3 months correlation between Microsoft and Shenkman is 0.06. Overlapping area represents the amount of risk that can be diversified away by holding Microsoft and Shenkman Floating Rate in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Shenkman Floating Rate and Microsoft 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 Microsoft are associated (or correlated) with Shenkman Floating. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Shenkman Floating Rate has no effect on the direction of Microsoft i.e., Microsoft and Shenkman Floating go up and down completely randomly.
Pair Corralation between Microsoft and Shenkman Floating
Given the investment horizon of 90 days Microsoft is expected to generate 13.16 times more return on investment than Shenkman Floating. However, Microsoft is 13.16 times more volatile than Shenkman Floating Rate. It trades about 0.06 of its potential returns per unit of risk. Shenkman Floating Rate is currently generating about 0.23 per unit of risk. If you would invest 42,973 in Microsoft on September 13, 2024 and sell it today you would earn a total of 1,974 from holding Microsoft or generate 4.59% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
Microsoft vs. Shenkman Floating Rate
Performance |
Timeline |
Microsoft |
Shenkman Floating Rate |
Microsoft and Shenkman Floating Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Microsoft and Shenkman Floating
The main advantage of trading using opposite Microsoft and Shenkman Floating positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Microsoft position performs unexpectedly, Shenkman Floating 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 Shenkman Floating will offset losses from the drop in Shenkman Floating's long position.Microsoft vs. Palo Alto Networks | Microsoft vs. Uipath Inc | Microsoft vs. Block Inc | Microsoft vs. Adobe Systems Incorporated |
Shenkman Floating vs. Shenkman Short Duration | Shenkman Floating vs. Shenkman Short Duration | Shenkman Floating vs. Shenkman Short Duration | Shenkman Floating vs. Shenkman Short Duration |
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 Price Transformation module to use Price Transformation models to analyze the depth of different equity instruments across global markets.
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