Correlation Between Samsung Electronics and 88 Energy
Can any of the company-specific risk be diversified away by investing in both Samsung Electronics and 88 Energy 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 88 Energy 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 88 Energy, you can compare the effects of market volatilities on Samsung Electronics and 88 Energy 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 88 Energy. Check out your portfolio center. Please also check ongoing floating volatility patterns of Samsung Electronics and 88 Energy.
Diversification Opportunities for Samsung Electronics and 88 Energy
0.3 | Correlation Coefficient |
Weak diversification
The 3 months correlation between Samsung and 88E is 0.3. Overlapping area represents the amount of risk that can be diversified away by holding Samsung Electronics Co and 88 Energy in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on 88 Energy 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 88 Energy. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of 88 Energy has no effect on the direction of Samsung Electronics i.e., Samsung Electronics and 88 Energy go up and down completely randomly.
Pair Corralation between Samsung Electronics and 88 Energy
Assuming the 90 days trading horizon Samsung Electronics Co is expected to generate 0.32 times more return on investment than 88 Energy. However, Samsung Electronics Co is 3.17 times less risky than 88 Energy. It trades about -0.01 of its potential returns per unit of risk. 88 Energy is currently generating about -0.04 per unit of risk. If you would invest 100,161 in Samsung Electronics Co on August 30, 2024 and sell it today you would lose (15,061) from holding Samsung Electronics Co or give up 15.04% of portfolio value over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Very Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
Samsung Electronics Co vs. 88 Energy
Performance |
| Timeline |
| Samsung Electronics |
| 88 Energy |
Samsung Electronics and 88 Energy Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Samsung Electronics and 88 Energy
The main advantage of trading using opposite Samsung Electronics and 88 Energy positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Samsung Electronics position performs unexpectedly, 88 Energy 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 88 Energy will offset losses from the drop in 88 Energy's long position.| Samsung Electronics vs. Ondine Biomedical | Samsung Electronics vs. Europa Metals | Samsung Electronics vs. Lendinvest PLC | Samsung Electronics vs. Neometals |
| 88 Energy vs. Amedeo Air Four | 88 Energy vs. Ryanair Holdings plc | 88 Energy vs. Worldwide Healthcare Trust | 88 Energy vs. PureTech Health plc |
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 Performance Analysis module to check effects of mean-variance optimization against your current asset allocation.
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