Correlation Between Consumers Energy and Korea Electric
Can any of the company-specific risk be diversified away by investing in both Consumers 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 Consumers 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 Consumers Energy and Korea Electric Power, you can compare the effects of market volatilities on Consumers 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 Consumers Energy with a short position of Korea Electric. Check out your portfolio center. Please also check ongoing floating volatility patterns of Consumers Energy and Korea Electric.
Diversification Opportunities for Consumers Energy and Korea Electric
0.64 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Consumers and Korea is 0.64. Overlapping area represents the amount of risk that can be diversified away by holding Consumers 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 Consumers 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 Consumers 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 Consumers Energy i.e., Consumers Energy and Korea Electric go up and down completely randomly.
Pair Corralation between Consumers Energy and Korea Electric
Assuming the 90 days trading horizon Consumers Energy is expected to generate 2.01 times less return on investment than Korea Electric. But when comparing it to its historical volatility, Consumers Energy is 1.43 times less risky than Korea Electric. It trades about 0.06 of its potential returns per unit of risk. Korea Electric Power is currently generating about 0.08 of returns per unit of risk over similar time horizon. If you would invest 681.00 in Korea Electric Power on December 30, 2024 and sell it today you would earn a total of 66.00 from holding Korea Electric Power or generate 9.69% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 100.0% |
Values | Daily Returns |
Consumers Energy vs. Korea Electric Power
Performance |
Timeline |
Consumers Energy |
Korea Electric Power |
Consumers Energy and Korea Electric Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Consumers Energy and Korea Electric
The main advantage of trading using opposite Consumers Energy and Korea Electric positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Consumers 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.Consumers Energy vs. Nextera Energy | Consumers Energy vs. Duke Energy | Consumers Energy vs. PGE Corp | Consumers Energy vs. Southern Company |
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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 Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.
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