Correlation Between Regions Financial and Sterling Bancorp
Can any of the company-specific risk be diversified away by investing in both Regions Financial and Sterling Bancorp 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 Regions Financial and Sterling Bancorp into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Regions Financial and Sterling Bancorp, you can compare the effects of market volatilities on Regions Financial and Sterling Bancorp 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 Regions Financial with a short position of Sterling Bancorp. Check out your portfolio center. Please also check ongoing floating volatility patterns of Regions Financial and Sterling Bancorp.
Diversification Opportunities for Regions Financial and Sterling Bancorp
-0.27 | Correlation Coefficient |
Very good diversification
The 3 months correlation between Regions and Sterling is -0.27. Overlapping area represents the amount of risk that can be diversified away by holding Regions Financial and Sterling Bancorp in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Sterling Bancorp and Regions Financial 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 Regions Financial are associated (or correlated) with Sterling Bancorp. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Sterling Bancorp has no effect on the direction of Regions Financial i.e., Regions Financial and Sterling Bancorp go up and down completely randomly.
Pair Corralation between Regions Financial and Sterling Bancorp
Allowing for the 90-day total investment horizon Regions Financial is expected to generate 0.85 times more return on investment than Sterling Bancorp. However, Regions Financial is 1.17 times less risky than Sterling Bancorp. It trades about 0.04 of its potential returns per unit of risk. Sterling Bancorp is currently generating about -0.01 per unit of risk. If you would invest 2,024 in Regions Financial on August 30, 2024 and sell it today you would earn a total of 701.00 from holding Regions Financial or generate 34.63% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Insignificant |
Accuracy | 99.8% |
Values | Daily Returns |
Regions Financial vs. Sterling Bancorp
Performance |
Timeline |
Regions Financial |
Sterling Bancorp |
Regions Financial and Sterling Bancorp Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Regions Financial and Sterling Bancorp
The main advantage of trading using opposite Regions Financial and Sterling Bancorp positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Regions Financial position performs unexpectedly, Sterling Bancorp 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 Sterling Bancorp will offset losses from the drop in Sterling Bancorp's long position.Regions Financial vs. KeyCorp | Regions Financial vs. Fifth Third Bancorp | Regions Financial vs. Zions Bancorporation | Regions Financial vs. Huntington Bancshares Incorporated |
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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 Pattern Recognition module to use different Pattern Recognition models to time the market across multiple global exchanges.
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