Correlation Between Bank of America and Strainsforpains
Can any of the company-specific risk be diversified away by investing in both Bank of America and Strainsforpains 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 Strainsforpains 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 Strainsforpains, you can compare the effects of market volatilities on Bank of America and Strainsforpains 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 Strainsforpains. Check out your portfolio center. Please also check ongoing floating volatility patterns of Bank of America and Strainsforpains.
Diversification Opportunities for Bank of America and Strainsforpains
-0.52 | Correlation Coefficient |
Excellent diversification
The 3 months correlation between Bank and Strainsforpains is -0.52. Overlapping area represents the amount of risk that can be diversified away by holding Bank of America and Strainsforpains in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Strainsforpains 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 Strainsforpains. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Strainsforpains has no effect on the direction of Bank of America i.e., Bank of America and Strainsforpains go up and down completely randomly.
Pair Corralation between Bank of America and Strainsforpains
Considering the 90-day investment horizon Bank of America is expected to generate 6.42 times less return on investment than Strainsforpains. But when comparing it to its historical volatility, Bank of America is 7.37 times less risky than Strainsforpains. It trades about 0.1 of its potential returns per unit of risk. Strainsforpains is currently generating about 0.09 of returns per unit of risk over similar time horizon. If you would invest 13.00 in Strainsforpains on September 3, 2024 and sell it today you would earn a total of 10.00 from holding Strainsforpains or generate 76.92% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Very Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Bank of America vs. Strainsforpains
Performance |
Timeline |
Bank of America |
Strainsforpains |
Bank of America and Strainsforpains Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Bank of America and Strainsforpains
The main advantage of trading using opposite Bank of America and Strainsforpains positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Bank of America position performs unexpectedly, Strainsforpains 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 Strainsforpains will offset losses from the drop in Strainsforpains' long position.Bank of America vs. Partner Communications | Bank of America vs. Merck Company | Bank of America vs. Western Midstream Partners | Bank of America vs. Edgewise Therapeutics |
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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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