Correlation Between Bank of America and Shinsung Delta
Can any of the company-specific risk be diversified away by investing in both Bank of America and Shinsung Delta 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 Shinsung Delta 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 Shinsung Delta Tech, you can compare the effects of market volatilities on Bank of America and Shinsung Delta 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 Shinsung Delta. Check out your portfolio center. Please also check ongoing floating volatility patterns of Bank of America and Shinsung Delta.
Diversification Opportunities for Bank of America and Shinsung Delta
0.16 | Correlation Coefficient |
Average diversification
The 3 months correlation between Bank and Shinsung is 0.16. Overlapping area represents the amount of risk that can be diversified away by holding Bank of America and Shinsung Delta Tech in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Shinsung Delta Tech 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 Shinsung Delta. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Shinsung Delta Tech has no effect on the direction of Bank of America i.e., Bank of America and Shinsung Delta go up and down completely randomly.
Pair Corralation between Bank of America and Shinsung Delta
Considering the 90-day investment horizon Bank of America is expected to generate 3.71 times less return on investment than Shinsung Delta. But when comparing it to its historical volatility, Bank of America is 2.99 times less risky than Shinsung Delta. It trades about 0.27 of its potential returns per unit of risk. Shinsung Delta Tech is currently generating about 0.33 of returns per unit of risk over similar time horizon. If you would invest 4,495,000 in Shinsung Delta Tech on August 29, 2024 and sell it today you would earn a total of 2,255,000 from holding Shinsung Delta Tech or generate 50.17% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Insignificant |
| Accuracy | 100.0% |
| Values | Daily Returns |
Bank of America vs. Shinsung Delta Tech
Performance |
| Timeline |
| Bank of America |
| Shinsung Delta Tech |
Bank of America and Shinsung Delta Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Bank of America and Shinsung Delta
The main advantage of trading using opposite Bank of America and Shinsung Delta positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Bank of America position performs unexpectedly, Shinsung Delta 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 Shinsung Delta will offset losses from the drop in Shinsung Delta'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 |
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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 Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.
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