Correlation Between Bank of America and Glassbox
Can any of the company-specific risk be diversified away by investing in both Bank of America and Glassbox 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 Glassbox 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 Glassbox, you can compare the effects of market volatilities on Bank of America and Glassbox 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 Glassbox. Check out your portfolio center. Please also check ongoing floating volatility patterns of Bank of America and Glassbox.
Diversification Opportunities for Bank of America and Glassbox
-0.26 | Correlation Coefficient |
Very good diversification
The 3 months correlation between Bank and Glassbox is -0.26. Overlapping area represents the amount of risk that can be diversified away by holding Bank of America and Glassbox in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Glassbox 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 Glassbox. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Glassbox has no effect on the direction of Bank of America i.e., Bank of America and Glassbox go up and down completely randomly.
Pair Corralation between Bank of America and Glassbox
Considering the 90-day investment horizon Bank of America is expected to generate 23.94 times more return on investment than Glassbox. However, Bank of America is 23.94 times more volatile than Glassbox. It trades about 0.27 of its potential returns per unit of risk. Glassbox is currently generating about -0.2 per unit of risk. If you would invest 4,262 in Bank of America on August 29, 2024 and sell it today you would earn a total of 523.50 from holding Bank of America or generate 12.28% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Insignificant |
| Accuracy | 26.09% |
| Values | Daily Returns |
Bank of America vs. Glassbox
Performance |
| Timeline |
| Bank of America |
| Glassbox |
Risk-Adjusted Performance
0 of 100
Weak | Strong |
Modest
Bank of America and Glassbox Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Bank of America and Glassbox
The main advantage of trading using opposite Bank of America and Glassbox positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Bank of America position performs unexpectedly, Glassbox 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 Glassbox will offset losses from the drop in Glassbox's long position.| Bank of America vs. Citigroup | Bank of America vs. Wells Fargo | Bank of America vs. Toronto Dominion Bank | Bank of America vs. JPMorgan Chase Co |
| Glassbox vs. MediPress Health Limited Partnership | Glassbox vs. Hiron Trade Investments Industrial | Glassbox vs. Clal Insurance Enterprises | Glassbox vs. Veridis Environment |
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 Backtesting module to avoid under-diversification and over-optimization by backtesting your portfolios.
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