Correlation Between Bengal Energy and Cardinal Energy
Can any of the company-specific risk be diversified away by investing in both Bengal Energy and Cardinal 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 Bengal Energy and Cardinal Energy into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Bengal Energy and Cardinal Energy, you can compare the effects of market volatilities on Bengal Energy and Cardinal 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 Bengal Energy with a short position of Cardinal Energy. Check out your portfolio center. Please also check ongoing floating volatility patterns of Bengal Energy and Cardinal Energy.
Diversification Opportunities for Bengal Energy and Cardinal Energy
0.29 | Correlation Coefficient |
Modest diversification
The 3 months correlation between Bengal and Cardinal is 0.29. Overlapping area represents the amount of risk that can be diversified away by holding Bengal Energy and Cardinal Energy in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Cardinal Energy and Bengal 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 Bengal Energy are associated (or correlated) with Cardinal Energy. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Cardinal Energy has no effect on the direction of Bengal Energy i.e., Bengal Energy and Cardinal Energy go up and down completely randomly.
Pair Corralation between Bengal Energy and Cardinal Energy
Assuming the 90 days horizon Bengal Energy is expected to generate 7.66 times more return on investment than Cardinal Energy. However, Bengal Energy is 7.66 times more volatile than Cardinal Energy. It trades about -0.03 of its potential returns per unit of risk. Cardinal Energy is currently generating about -0.24 per unit of risk. If you would invest 0.80 in Bengal Energy on November 3, 2024 and sell it today you would lose (0.11) from holding Bengal Energy or give up 13.75% of portfolio value over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Very Weak |
| Accuracy | 91.3% |
| Values | Daily Returns |
Bengal Energy vs. Cardinal Energy
Performance |
| Timeline |
| Bengal Energy |
| Cardinal Energy |
Bengal Energy and Cardinal Energy Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Bengal Energy and Cardinal Energy
The main advantage of trading using opposite Bengal Energy and Cardinal Energy positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Bengal Energy position performs unexpectedly, Cardinal 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 Cardinal Energy will offset losses from the drop in Cardinal Energy's long position.| Bengal Energy vs. Agnico Eagle Mines | Bengal Energy vs. Osisko Gold Ro | Bengal Energy vs. Gold Fields Ltd | Bengal Energy vs. AngloGold Ashanti plc |
| Cardinal Energy vs. Tamarack Valley Energy | Cardinal Energy vs. Pine Cliff Energy | Cardinal Energy vs. MEG Energy Corp | Cardinal Energy vs. Headwater Exploration |
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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