Correlation Between Microsoft and Guangshen Railway
Can any of the company-specific risk be diversified away by investing in both Microsoft and Guangshen Railway 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 Guangshen Railway into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Microsoft and Guangshen Railway, you can compare the effects of market volatilities on Microsoft and Guangshen Railway 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 Guangshen Railway. Check out your portfolio center. Please also check ongoing floating volatility patterns of Microsoft and Guangshen Railway.
Diversification Opportunities for Microsoft and Guangshen Railway
-0.29 | Correlation Coefficient |
Very good diversification
The 3 months correlation between Microsoft and Guangshen is -0.29. Overlapping area represents the amount of risk that can be diversified away by holding Microsoft and Guangshen Railway in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Guangshen Railway 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 Guangshen Railway. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Guangshen Railway has no effect on the direction of Microsoft i.e., Microsoft and Guangshen Railway go up and down completely randomly.
Pair Corralation between Microsoft and Guangshen Railway
Given the investment horizon of 90 days Microsoft is expected to generate 6.49 times less return on investment than Guangshen Railway. But when comparing it to its historical volatility, Microsoft is 8.0 times less risky than Guangshen Railway. It trades about 0.09 of its potential returns per unit of risk. Guangshen Railway is currently generating about 0.07 of returns per unit of risk over similar time horizon. If you would invest 15.00 in Guangshen Railway on September 12, 2024 and sell it today you would earn a total of 9.00 from holding Guangshen Railway or generate 60.0% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Insignificant |
| Accuracy | 52.87% |
| Values | Daily Returns |
Microsoft vs. Guangshen Railway
Performance |
| Timeline |
| Microsoft |
| Guangshen Railway |
Microsoft and Guangshen Railway Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Microsoft and Guangshen Railway
The main advantage of trading using opposite Microsoft and Guangshen Railway positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Microsoft position performs unexpectedly, Guangshen Railway 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 Guangshen Railway will offset losses from the drop in Guangshen Railway's long position.| Microsoft vs. Palo Alto Networks | Microsoft vs. Uipath Inc | Microsoft vs. Block Inc | Microsoft vs. Adobe Systems Incorporated |
| Guangshen Railway vs. Trinity Industries | Guangshen Railway vs. CSX Corporation | Guangshen Railway vs. Norfolk Southern | Guangshen Railway vs. Greenbrier Companies |
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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