Correlation Between Bank of America and Shinhan Inverse
Can any of the company-specific risk be diversified away by investing in both Bank of America and Shinhan Inverse 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 Shinhan Inverse 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 Shinhan Inverse Copper, you can compare the effects of market volatilities on Bank of America and Shinhan Inverse 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 Shinhan Inverse. Check out your portfolio center. Please also check ongoing floating volatility patterns of Bank of America and Shinhan Inverse.
Diversification Opportunities for Bank of America and Shinhan Inverse
0.12 | Correlation Coefficient |
Average diversification
The 3 months correlation between Bank and Shinhan is 0.12. Overlapping area represents the amount of risk that can be diversified away by holding Bank of America and Shinhan Inverse Copper in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Shinhan Inverse Copper 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 Shinhan Inverse. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Shinhan Inverse Copper has no effect on the direction of Bank of America i.e., Bank of America and Shinhan Inverse go up and down completely randomly.
Pair Corralation between Bank of America and Shinhan Inverse
Considering the 90-day investment horizon Bank of America is expected to under-perform the Shinhan Inverse. But the stock apears to be less risky and, when comparing its historical volatility, Bank of America is 1.11 times less risky than Shinhan Inverse. The stock trades about -0.16 of its potential returns per unit of risk. The Shinhan Inverse Copper is currently generating about -0.1 of returns per unit of risk over similar time horizon. If you would invest 542,500 in Shinhan Inverse Copper on December 11, 2024 and sell it today you would lose (38,500) from holding Shinhan Inverse Copper or give up 7.1% of portfolio value over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Insignificant |
| Accuracy | 92.5% |
| Values | Daily Returns |
Bank of America vs. Shinhan Inverse Copper
Performance |
| Timeline |
| Bank of America |
| Shinhan Inverse Copper |
Bank of America and Shinhan Inverse Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Bank of America and Shinhan Inverse
The main advantage of trading using opposite Bank of America and Shinhan Inverse positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Bank of America position performs unexpectedly, Shinhan Inverse 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 Shinhan Inverse will offset losses from the drop in Shinhan Inverse's long position.| Bank of America vs. Citigroup | Bank of America vs. Wells Fargo | Bank of America vs. Nu Holdings | Bank of America vs. HSBC Holdings PLC |
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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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