Correlation Between Bank of New York and SBI Holdings
Can any of the company-specific risk be diversified away by investing in both Bank of New York and SBI Holdings 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 New York and SBI Holdings into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Bank of New and SBI Holdings, you can compare the effects of market volatilities on Bank of New York and SBI Holdings 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 New York with a short position of SBI Holdings. Check out your portfolio center. Please also check ongoing floating volatility patterns of Bank of New York and SBI Holdings.
Diversification Opportunities for Bank of New York and SBI Holdings
-0.61 | Correlation Coefficient |
Excellent diversification
The 3 months correlation between Bank and SBI is -0.61. Overlapping area represents the amount of risk that can be diversified away by holding Bank of New and SBI Holdings in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on SBI Holdings and Bank of New York 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 New are associated (or correlated) with SBI Holdings. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of SBI Holdings has no effect on the direction of Bank of New York i.e., Bank of New York and SBI Holdings go up and down completely randomly.
Pair Corralation between Bank of New York and SBI Holdings
Allowing for the 90-day total investment horizon Bank of New is expected to generate 0.25 times more return on investment than SBI Holdings. However, Bank of New is 4.07 times less risky than SBI Holdings. It trades about 0.22 of its potential returns per unit of risk. SBI Holdings is currently generating about 0.01 per unit of risk. If you would invest 5,745 in Bank of New on August 24, 2024 and sell it today you would earn a total of 2,143 from holding Bank of New or generate 37.3% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
Bank of New vs. SBI Holdings
Performance |
| Timeline |
| Bank of New York |
| SBI Holdings |
Bank of New York and SBI Holdings Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Bank of New York and SBI Holdings
The main advantage of trading using opposite Bank of New York and SBI Holdings positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Bank of New York position performs unexpectedly, SBI Holdings 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 SBI Holdings will offset losses from the drop in SBI Holdings' long position.| Bank of New York vs. Northern Trust | Bank of New York vs. Invesco Plc | Bank of New York vs. Franklin Resources | Bank of New York vs. T Rowe Price |
| SBI Holdings vs. Voya Financial | SBI Holdings vs. Lendingtree | SBI Holdings vs. B Riley Financial | SBI Holdings vs. B Riley Financial |
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 Watchlist Optimization module to optimize watchlists to build efficient portfolios or rebalance existing positions based on the mean-variance optimization algorithm.
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