Correlation Between Microsoft and Retail Food
Can any of the company-specific risk be diversified away by investing in both Microsoft and Retail Food 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 Retail Food into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Microsoft and Retail Food Group, you can compare the effects of market volatilities on Microsoft and Retail Food 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 Retail Food. Check out your portfolio center. Please also check ongoing floating volatility patterns of Microsoft and Retail Food.
Diversification Opportunities for Microsoft and Retail Food
-0.1 | Correlation Coefficient |
Good diversification
The 3 months correlation between Microsoft and Retail is -0.1. Overlapping area represents the amount of risk that can be diversified away by holding Microsoft and Retail Food Group in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Retail Food Group 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 Retail Food. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Retail Food Group has no effect on the direction of Microsoft i.e., Microsoft and Retail Food go up and down completely randomly.
Pair Corralation between Microsoft and Retail Food
Given the investment horizon of 90 days Microsoft is expected to under-perform the Retail Food. But the stock apears to be less risky and, when comparing its historical volatility, Microsoft is 1.63 times less risky than Retail Food. The stock trades about 0.0 of its potential returns per unit of risk. The Retail Food Group is currently generating about 0.18 of returns per unit of risk over similar time horizon. If you would invest 6.60 in Retail Food Group on August 29, 2024 and sell it today you would earn a total of 0.70 from holding Retail Food Group or generate 10.61% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Insignificant |
| Accuracy | 95.65% |
| Values | Daily Returns |
Microsoft vs. Retail Food Group
Performance |
| Timeline |
| Microsoft |
| Retail Food Group |
Microsoft and Retail Food Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Microsoft and Retail Food
The main advantage of trading using opposite Microsoft and Retail Food positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Microsoft position performs unexpectedly, Retail Food 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 Retail Food will offset losses from the drop in Retail Food's long position.| Microsoft vs. Palo Alto Networks | Microsoft vs. Uipath Inc | Microsoft vs. Block Inc | Microsoft vs. Adobe Systems Incorporated |
| Retail Food vs. Autosports Group | Retail Food vs. AiMedia Technologies | Retail Food vs. BKI Investment | Retail Food vs. ARN Media Limited |
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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