Correlation Between Regions Financial and Camden National
Can any of the company-specific risk be diversified away by investing in both Regions Financial and Camden National 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 Camden National into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Regions Financial and Camden National, you can compare the effects of market volatilities on Regions Financial and Camden National 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 Camden National. Check out your portfolio center. Please also check ongoing floating volatility patterns of Regions Financial and Camden National.
Diversification Opportunities for Regions Financial and Camden National
0.83 | Correlation Coefficient |
Very poor diversification
The 3 months correlation between Regions and Camden is 0.83. Overlapping area represents the amount of risk that can be diversified away by holding Regions Financial and Camden National in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Camden National 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 Camden National. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Camden National has no effect on the direction of Regions Financial i.e., Regions Financial and Camden National go up and down completely randomly.
Pair Corralation between Regions Financial and Camden National
Allowing for the 90-day total investment horizon Regions Financial is expected to under-perform the Camden National. In addition to that, Regions Financial is 1.06 times more volatile than Camden National. It trades about -0.09 of its total potential returns per unit of risk. Camden National is currently generating about -0.02 per unit of volatility. If you would invest 4,236 in Camden National on December 28, 2024 and sell it today you would lose (91.00) from holding Camden National or give up 2.15% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Strong |
Accuracy | 98.36% |
Values | Daily Returns |
Regions Financial vs. Camden National
Performance |
Timeline |
Regions Financial |
Camden National |
Regions Financial and Camden National Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Regions Financial and Camden National
The main advantage of trading using opposite Regions Financial and Camden National positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Regions Financial position performs unexpectedly, Camden National 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 Camden National will offset losses from the drop in Camden National's long position.Regions Financial vs. Home Bancorp | Regions Financial vs. Rhinebeck Bancorp | Regions Financial vs. LINKBANCORP | Regions Financial vs. Magyar Bancorp |
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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 My Watchlist Analysis module to analyze my current watchlist and to refresh optimization strategy. Macroaxis watchlist is based on self-learning algorithm to remember stocks you like.
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