Correlation Between Samsung Electronics and Mapfre SA
Can any of the company-specific risk be diversified away by investing in both Samsung Electronics and Mapfre SA 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 Samsung Electronics and Mapfre SA into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Samsung Electronics Co and Mapfre SA, you can compare the effects of market volatilities on Samsung Electronics and Mapfre SA 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 Samsung Electronics with a short position of Mapfre SA. Check out your portfolio center. Please also check ongoing floating volatility patterns of Samsung Electronics and Mapfre SA.
Diversification Opportunities for Samsung Electronics and Mapfre SA
-0.79 | Correlation Coefficient |
Pay attention - limited upside
The 3 months correlation between Samsung and Mapfre is -0.79. Overlapping area represents the amount of risk that can be diversified away by holding Samsung Electronics Co and Mapfre SA in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Mapfre SA and Samsung Electronics 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 Samsung Electronics Co are associated (or correlated) with Mapfre SA. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Mapfre SA has no effect on the direction of Samsung Electronics i.e., Samsung Electronics and Mapfre SA go up and down completely randomly.
Pair Corralation between Samsung Electronics and Mapfre SA
Assuming the 90 days trading horizon Samsung Electronics Co is expected to under-perform the Mapfre SA. In addition to that, Samsung Electronics is 1.15 times more volatile than Mapfre SA. It trades about -0.04 of its total potential returns per unit of risk. Mapfre SA is currently generating about 0.09 per unit of volatility. If you would invest 177.00 in Mapfre SA on September 14, 2024 and sell it today you would earn a total of 71.00 from holding Mapfre SA or generate 40.11% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
Samsung Electronics Co vs. Mapfre SA
Performance |
| Timeline |
| Samsung Electronics |
| Mapfre SA |
Samsung Electronics and Mapfre SA Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Samsung Electronics and Mapfre SA
The main advantage of trading using opposite Samsung Electronics and Mapfre SA positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Samsung Electronics position performs unexpectedly, Mapfre SA 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 Mapfre SA will offset losses from the drop in Mapfre SA's long position.| Samsung Electronics vs. Boyd Gaming | Samsung Electronics vs. Soken Chemical Engineering | Samsung Electronics vs. GAMESTOP | Samsung Electronics vs. CHEMICAL INDUSTRIES |
| Mapfre SA vs. First American Financial | Mapfre SA vs. MGIC Investment | Mapfre SA vs. Lancashire Holdings Limited | Mapfre SA vs. Trisura Group |
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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