Correlation Between Clean Carbon and BNP Paribas
Can any of the company-specific risk be diversified away by investing in both Clean Carbon and BNP Paribas 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 Clean Carbon and BNP Paribas into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Clean Carbon Energy and BNP Paribas Bank, you can compare the effects of market volatilities on Clean Carbon and BNP Paribas 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 Clean Carbon with a short position of BNP Paribas. Check out your portfolio center. Please also check ongoing floating volatility patterns of Clean Carbon and BNP Paribas.
Diversification Opportunities for Clean Carbon and BNP Paribas
0.36 | Correlation Coefficient |
Weak diversification
The 3 months correlation between Clean and BNP is 0.36. Overlapping area represents the amount of risk that can be diversified away by holding Clean Carbon Energy and BNP Paribas Bank in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on BNP Paribas Bank and Clean Carbon 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 Clean Carbon Energy are associated (or correlated) with BNP Paribas. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of BNP Paribas Bank has no effect on the direction of Clean Carbon i.e., Clean Carbon and BNP Paribas go up and down completely randomly.
Pair Corralation between Clean Carbon and BNP Paribas
Assuming the 90 days trading horizon Clean Carbon Energy is expected to generate 5.46 times more return on investment than BNP Paribas. However, Clean Carbon is 5.46 times more volatile than BNP Paribas Bank. It trades about 0.38 of its potential returns per unit of risk. BNP Paribas Bank is currently generating about -0.05 per unit of risk. If you would invest 24.00 in Clean Carbon Energy on November 5, 2024 and sell it today you would earn a total of 16.00 from holding Clean Carbon Energy or generate 66.67% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Very Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
Clean Carbon Energy vs. BNP Paribas Bank
Performance |
| Timeline |
| Clean Carbon Energy |
| BNP Paribas Bank |
Clean Carbon and BNP Paribas Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Clean Carbon and BNP Paribas
The main advantage of trading using opposite Clean Carbon and BNP Paribas positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Clean Carbon position performs unexpectedly, BNP Paribas 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 BNP Paribas will offset losses from the drop in BNP Paribas' long position.| Clean Carbon vs. Asseco Business Solutions | Clean Carbon vs. Movie Games SA | Clean Carbon vs. Altustfi | Clean Carbon vs. 3R Games SA |
| BNP Paribas vs. UniCredit SpA | BNP Paribas vs. Santander Bank Polska | BNP Paribas vs. Bank Polska Kasa | BNP Paribas vs. ING Bank lski |
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 Watchlist Optimization module to optimize watchlists to build efficient portfolios or rebalance existing positions based on the mean-variance optimization algorithm.
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