Correlation Between Petroleo Brasileiro and Swan Defined
Can any of the company-specific risk be diversified away by investing in both Petroleo Brasileiro and Swan Defined 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 Petroleo Brasileiro and Swan Defined into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Petroleo Brasileiro Petrobras and Swan Defined Risk, you can compare the effects of market volatilities on Petroleo Brasileiro and Swan Defined 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 Petroleo Brasileiro with a short position of Swan Defined. Check out your portfolio center. Please also check ongoing floating volatility patterns of Petroleo Brasileiro and Swan Defined.
Diversification Opportunities for Petroleo Brasileiro and Swan Defined
0.39 | Correlation Coefficient |
Weak diversification
The 3 months correlation between Petroleo and Swan is 0.39. Overlapping area represents the amount of risk that can be diversified away by holding Petroleo Brasileiro Petrobras and Swan Defined Risk in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Swan Defined Risk and Petroleo Brasileiro 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 Petroleo Brasileiro Petrobras are associated (or correlated) with Swan Defined. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Swan Defined Risk has no effect on the direction of Petroleo Brasileiro i.e., Petroleo Brasileiro and Swan Defined go up and down completely randomly.
Pair Corralation between Petroleo Brasileiro and Swan Defined
Considering the 90-day investment horizon Petroleo Brasileiro Petrobras is expected to generate 2.5 times more return on investment than Swan Defined. However, Petroleo Brasileiro is 2.5 times more volatile than Swan Defined Risk. It trades about 0.0 of its potential returns per unit of risk. Swan Defined Risk is currently generating about -0.16 per unit of risk. If you would invest 1,314 in Petroleo Brasileiro Petrobras on September 23, 2024 and sell it today you would lose (10.00) from holding Petroleo Brasileiro Petrobras or give up 0.76% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Very Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Petroleo Brasileiro Petrobras vs. Swan Defined Risk
Performance |
Timeline |
Petroleo Brasileiro |
Swan Defined Risk |
Petroleo Brasileiro and Swan Defined Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Petroleo Brasileiro and Swan Defined
The main advantage of trading using opposite Petroleo Brasileiro and Swan Defined positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Petroleo Brasileiro position performs unexpectedly, Swan Defined 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 Swan Defined will offset losses from the drop in Swan Defined's long position.Petroleo Brasileiro vs. Shell PLC ADR | Petroleo Brasileiro vs. Suncor Energy | Petroleo Brasileiro vs. TotalEnergies SE ADR | Petroleo Brasileiro vs. Equinor ASA ADR |
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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 Equity Forecasting module to use basic forecasting models to generate price predictions and determine price momentum.
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