Correlation Between Microsoft and Inverse Emerging
Can any of the company-specific risk be diversified away by investing in both Microsoft and Inverse Emerging 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 Inverse Emerging into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Microsoft and Inverse Emerging Markets, you can compare the effects of market volatilities on Microsoft and Inverse Emerging 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 Inverse Emerging. Check out your portfolio center. Please also check ongoing floating volatility patterns of Microsoft and Inverse Emerging.
Diversification Opportunities for Microsoft and Inverse Emerging
0.4 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between Microsoft and Inverse is 0.4. Overlapping area represents the amount of risk that can be diversified away by holding Microsoft and Inverse Emerging Markets in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Inverse Emerging Markets 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 Inverse Emerging. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Inverse Emerging Markets has no effect on the direction of Microsoft i.e., Microsoft and Inverse Emerging go up and down completely randomly.
Pair Corralation between Microsoft and Inverse Emerging
Given the investment horizon of 90 days Microsoft is expected to generate 0.47 times more return on investment than Inverse Emerging. However, Microsoft is 2.12 times less risky than Inverse Emerging. It trades about 0.01 of its potential returns per unit of risk. Inverse Emerging Markets is currently generating about -0.04 per unit of risk. If you would invest 44,667 in Microsoft on September 14, 2024 and sell it today you would earn a total of 60.00 from holding Microsoft or generate 0.13% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
Microsoft vs. Inverse Emerging Markets
Performance |
| Timeline |
| Microsoft |
| Inverse Emerging Markets |
Microsoft and Inverse Emerging Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Microsoft and Inverse Emerging
The main advantage of trading using opposite Microsoft and Inverse Emerging positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Microsoft position performs unexpectedly, Inverse Emerging 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 Inverse Emerging will offset losses from the drop in Inverse Emerging's long position.| Microsoft vs. Palo Alto Networks | Microsoft vs. Uipath Inc | Microsoft vs. Block Inc | Microsoft vs. Adobe Systems Incorporated |
| Inverse Emerging vs. Basic Materials Fund | Inverse Emerging vs. Basic Materials Fund | Inverse Emerging vs. Banking Fund Class | Inverse Emerging vs. Basic Materials Fund |
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 Equity Forecasting module to use basic forecasting models to generate price predictions and determine price momentum.
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