Correlation Between Agilent Technologies and BTG Pactual
Can any of the company-specific risk be diversified away by investing in both Agilent Technologies and BTG Pactual 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 Agilent Technologies and BTG Pactual into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Agilent Technologies and BTG Pactual Logstica, you can compare the effects of market volatilities on Agilent Technologies and BTG Pactual 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 Agilent Technologies with a short position of BTG Pactual. Check out your portfolio center. Please also check ongoing floating volatility patterns of Agilent Technologies and BTG Pactual.
Diversification Opportunities for Agilent Technologies and BTG Pactual
-0.7 | Correlation Coefficient |
Excellent diversification
The 3 months correlation between Agilent and BTG is -0.7. Overlapping area represents the amount of risk that can be diversified away by holding Agilent Technologies and BTG Pactual Logstica in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on BTG Pactual Logstica and Agilent Technologies 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 Agilent Technologies are associated (or correlated) with BTG Pactual. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of BTG Pactual Logstica has no effect on the direction of Agilent Technologies i.e., Agilent Technologies and BTG Pactual go up and down completely randomly.
Pair Corralation between Agilent Technologies and BTG Pactual
Assuming the 90 days trading horizon Agilent Technologies is expected to generate 1.91 times more return on investment than BTG Pactual. However, Agilent Technologies is 1.91 times more volatile than BTG Pactual Logstica. It trades about 0.09 of its potential returns per unit of risk. BTG Pactual Logstica is currently generating about -0.21 per unit of risk. If you would invest 37,126 in Agilent Technologies on September 4, 2024 and sell it today you would earn a total of 3,161 from holding Agilent Technologies or generate 8.51% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Agilent Technologies vs. BTG Pactual Logstica
Performance |
Timeline |
Agilent Technologies |
BTG Pactual Logstica |
Agilent Technologies and BTG Pactual Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Agilent Technologies and BTG Pactual
The main advantage of trading using opposite Agilent Technologies and BTG Pactual positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Agilent Technologies position performs unexpectedly, BTG Pactual 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 BTG Pactual will offset losses from the drop in BTG Pactual's long position.Agilent Technologies vs. DexCom Inc | Agilent Technologies vs. Diagnsticos da Amrica | Agilent Technologies vs. Fundo Investimento Imobiliario | Agilent Technologies vs. Fras le SA |
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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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