Correlation Between Bank of America and PXP Energy
Can any of the company-specific risk be diversified away by investing in both Bank of America and PXP 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 Bank of America and PXP Energy into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Bank of America and PXP Energy Corp, you can compare the effects of market volatilities on Bank of America and PXP 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 Bank of America with a short position of PXP Energy. Check out your portfolio center. Please also check ongoing floating volatility patterns of Bank of America and PXP Energy.
Diversification Opportunities for Bank of America and PXP Energy
-0.75 | Correlation Coefficient |
Pay attention - limited upside
The 3 months correlation between Bank and PXP is -0.75. Overlapping area represents the amount of risk that can be diversified away by holding Bank of America and PXP Energy Corp in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on PXP Energy Corp and Bank of America 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 Bank of America are associated (or correlated) with PXP Energy. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of PXP Energy Corp has no effect on the direction of Bank of America i.e., Bank of America and PXP Energy go up and down completely randomly.
Pair Corralation between Bank of America and PXP Energy
Considering the 90-day investment horizon Bank of America is expected to generate 0.3 times more return on investment than PXP Energy. However, Bank of America is 3.36 times less risky than PXP Energy. It trades about 0.11 of its potential returns per unit of risk. PXP Energy Corp is currently generating about 0.01 per unit of risk. If you would invest 3,949 in Bank of America on August 29, 2024 and sell it today you would earn a total of 826.00 from holding Bank of America or generate 20.92% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Weak |
| Accuracy | 97.6% |
| Values | Daily Returns |
Bank of America vs. PXP Energy Corp
Performance |
| Timeline |
| Bank of America |
| PXP Energy Corp |
Bank of America and PXP Energy Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Bank of America and PXP Energy
The main advantage of trading using opposite Bank of America and PXP Energy positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Bank of America position performs unexpectedly, PXP 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 PXP Energy will offset losses from the drop in PXP Energy's long position.| Bank of America vs. Nu Holdings | Bank of America vs. HSBC Holdings PLC | Bank of America vs. Bank of Nova |
| PXP Energy vs. Concepcion Industrial Corp | PXP Energy vs. Philex Mining Corp | PXP Energy vs. Transpacific Broadband Group | PXP Energy vs. Converge Information Communications |
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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