Correlation Between CMS Energy and Korea Electric
Can any of the company-specific risk be diversified away by investing in both CMS Energy and Korea Electric 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 CMS Energy and Korea Electric into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between CMS Energy and Korea Electric Power, you can compare the effects of market volatilities on CMS Energy and Korea Electric 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 CMS Energy with a short position of Korea Electric. Check out your portfolio center. Please also check ongoing floating volatility patterns of CMS Energy and Korea Electric.
Diversification Opportunities for CMS Energy and Korea Electric
-0.48 | Correlation Coefficient |
Very good diversification
The 3 months correlation between CMS and Korea is -0.48. Overlapping area represents the amount of risk that can be diversified away by holding CMS Energy and Korea Electric Power in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Korea Electric Power and CMS Energy 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 CMS Energy are associated (or correlated) with Korea Electric. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Korea Electric Power has no effect on the direction of CMS Energy i.e., CMS Energy and Korea Electric go up and down completely randomly.
Pair Corralation between CMS Energy and Korea Electric
Assuming the 90 days trading horizon CMS Energy is expected to generate 1.19 times less return on investment than Korea Electric. But when comparing it to its historical volatility, CMS Energy is 1.58 times less risky than Korea Electric. It trades about 0.03 of its potential returns per unit of risk. Korea Electric Power is currently generating about 0.02 of returns per unit of risk over similar time horizon. If you would invest 764.00 in Korea Electric Power on August 24, 2024 and sell it today you would earn a total of 94.00 from holding Korea Electric Power or generate 12.3% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Very Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
CMS Energy vs. Korea Electric Power
Performance |
| Timeline |
| CMS Energy |
| Korea Electric Power |
CMS Energy and Korea Electric Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with CMS Energy and Korea Electric
The main advantage of trading using opposite CMS Energy and Korea Electric positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if CMS Energy position performs unexpectedly, Korea Electric 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 Korea Electric will offset losses from the drop in Korea Electric's long position.| CMS Energy vs. Entergy Texas | CMS Energy vs. Duke Energy | CMS Energy vs. Spire Inc | CMS Energy vs. Consumers Energy |
| Korea Electric vs. Enel Chile SA | Korea Electric vs. Centrais Eltricas Brasileiras | Korea Electric vs. Central Puerto SA | Korea Electric vs. CMS Energy |
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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