Correlation Between Sumitomo Mitsui and Equinix
Can any of the company-specific risk be diversified away by investing in both Sumitomo Mitsui and Equinix 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 Sumitomo Mitsui and Equinix into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Sumitomo Mitsui Financial and Equinix, you can compare the effects of market volatilities on Sumitomo Mitsui and Equinix 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 Sumitomo Mitsui with a short position of Equinix. Check out your portfolio center. Please also check ongoing floating volatility patterns of Sumitomo Mitsui and Equinix.
Diversification Opportunities for Sumitomo Mitsui and Equinix
0.69 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Sumitomo and Equinix is 0.69. Overlapping area represents the amount of risk that can be diversified away by holding Sumitomo Mitsui Financial and Equinix in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Equinix and Sumitomo Mitsui 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 Sumitomo Mitsui Financial are associated (or correlated) with Equinix. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Equinix has no effect on the direction of Sumitomo Mitsui i.e., Sumitomo Mitsui and Equinix go up and down completely randomly.
Pair Corralation between Sumitomo Mitsui and Equinix
Assuming the 90 days trading horizon Sumitomo Mitsui Financial is expected to generate 0.64 times more return on investment than Equinix. However, Sumitomo Mitsui Financial is 1.57 times less risky than Equinix. It trades about 0.38 of its potential returns per unit of risk. Equinix is currently generating about 0.2 per unit of risk. If you would invest 7,378 in Sumitomo Mitsui Financial on September 1, 2024 and sell it today you would earn a total of 1,034 from holding Sumitomo Mitsui Financial or generate 14.01% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Significant |
| Accuracy | 95.24% |
| Values | Daily Returns |
Sumitomo Mitsui Financial vs. Equinix
Performance |
| Timeline |
| Sumitomo Mitsui Financial |
| Equinix |
Sumitomo Mitsui and Equinix Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Sumitomo Mitsui and Equinix
The main advantage of trading using opposite Sumitomo Mitsui and Equinix positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Sumitomo Mitsui position performs unexpectedly, Equinix 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 Equinix will offset losses from the drop in Equinix's long position.| Sumitomo Mitsui vs. Mitsubishi UFJ Financial | Sumitomo Mitsui vs. Fras le SA | Sumitomo Mitsui vs. Western Digital | Sumitomo Mitsui vs. Energisa SA |
| Equinix vs. Unity Software | Equinix vs. Cognizant Technology Solutions | Equinix vs. Uber Technologies | Equinix vs. BIONTECH SE DRN |
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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