Correlation Between Mifflinburg Bancorp and Bank of Botetourt
Can any of the company-specific risk be diversified away by investing in both Mifflinburg Bancorp and Bank of Botetourt 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 Mifflinburg Bancorp and Bank of Botetourt into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Mifflinburg Bancorp and Bank of Botetourt, you can compare the effects of market volatilities on Mifflinburg Bancorp and Bank of Botetourt 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 Mifflinburg Bancorp with a short position of Bank of Botetourt. Check out your portfolio center. Please also check ongoing floating volatility patterns of Mifflinburg Bancorp and Bank of Botetourt.
Diversification Opportunities for Mifflinburg Bancorp and Bank of Botetourt
0.33 | Correlation Coefficient |
Weak diversification
The 3 months correlation between Mifflinburg and Bank is 0.33. Overlapping area represents the amount of risk that can be diversified away by holding Mifflinburg Bancorp and Bank of Botetourt in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Bank of Botetourt and Mifflinburg Bancorp 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 Mifflinburg Bancorp are associated (or correlated) with Bank of Botetourt. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Bank of Botetourt has no effect on the direction of Mifflinburg Bancorp i.e., Mifflinburg Bancorp and Bank of Botetourt go up and down completely randomly.
Pair Corralation between Mifflinburg Bancorp and Bank of Botetourt
Given the investment horizon of 90 days Mifflinburg Bancorp is expected to under-perform the Bank of Botetourt. In addition to that, Mifflinburg Bancorp is 3.24 times more volatile than Bank of Botetourt. It trades about -0.02 of its total potential returns per unit of risk. Bank of Botetourt is currently generating about 0.13 per unit of volatility. If you would invest 2,840 in Bank of Botetourt on August 25, 2024 and sell it today you would earn a total of 374.00 from holding Bank of Botetourt or generate 13.17% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Very Weak |
Accuracy | 94.44% |
Values | Daily Returns |
Mifflinburg Bancorp vs. Bank of Botetourt
Performance |
Timeline |
Mifflinburg Bancorp |
Bank of Botetourt |
Mifflinburg Bancorp and Bank of Botetourt Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Mifflinburg Bancorp and Bank of Botetourt
The main advantage of trading using opposite Mifflinburg Bancorp and Bank of Botetourt positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Mifflinburg Bancorp position performs unexpectedly, Bank of Botetourt 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 Bank of Botetourt will offset losses from the drop in Bank of Botetourt's long position.Mifflinburg Bancorp vs. Mauch Chunk Trust | Mifflinburg Bancorp vs. Juniata Valley Financial | Mifflinburg Bancorp vs. MBT Bancshares |
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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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