Correlation Between CNX Resources and Epsilon Energy
Can any of the company-specific risk be diversified away by investing in both CNX Resources and Epsilon 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 CNX Resources and Epsilon Energy into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between CNX Resources Corp and Epsilon Energy, you can compare the effects of market volatilities on CNX Resources and Epsilon 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 CNX Resources with a short position of Epsilon Energy. Check out your portfolio center. Please also check ongoing floating volatility patterns of CNX Resources and Epsilon Energy.
Diversification Opportunities for CNX Resources and Epsilon Energy
-0.73 | Correlation Coefficient |
Pay attention - limited upside
The 3 months correlation between CNX and Epsilon is -0.73. Overlapping area represents the amount of risk that can be diversified away by holding CNX Resources Corp and Epsilon Energy in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Epsilon Energy and CNX Resources 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 CNX Resources Corp are associated (or correlated) with Epsilon Energy. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Epsilon Energy has no effect on the direction of CNX Resources i.e., CNX Resources and Epsilon Energy go up and down completely randomly.
Pair Corralation between CNX Resources and Epsilon Energy
Considering the 90-day investment horizon CNX Resources Corp is expected to generate 0.95 times more return on investment than Epsilon Energy. However, CNX Resources Corp is 1.05 times less risky than Epsilon Energy. It trades about 0.01 of its potential returns per unit of risk. Epsilon Energy is currently generating about -0.09 per unit of risk. If you would invest 3,014 in CNX Resources Corp on November 18, 2024 and sell it today you would earn a total of 5.00 from holding CNX Resources Corp or generate 0.17% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
CNX Resources Corp vs. Epsilon Energy
Performance |
| Timeline |
| CNX Resources Corp |
| Epsilon Energy |
CNX Resources and Epsilon Energy Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with CNX Resources and Epsilon Energy
The main advantage of trading using opposite CNX Resources and Epsilon Energy positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if CNX Resources position performs unexpectedly, Epsilon 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 Epsilon Energy will offset losses from the drop in Epsilon Energy's long position.| CNX Resources vs. Epsilon Energy | CNX Resources vs. Gulfport Energy Operating | CNX Resources vs. GeoPark | CNX Resources vs. MV Oil Trust |
| Epsilon Energy vs. Vaalco Energy | Epsilon Energy vs. PHX Minerals | Epsilon Energy vs. Northern Oil Gas | Epsilon Energy vs. Granite Ridge Resources |
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 Pattern Recognition module to use different Pattern Recognition models to time the market across multiple global exchanges.
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