Correlation Between Byline Bancorp and William Penn
Can any of the company-specific risk be diversified away by investing in both Byline Bancorp and William Penn 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 Byline Bancorp and William Penn into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Byline Bancorp and William Penn Bancorp, you can compare the effects of market volatilities on Byline Bancorp and William Penn 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 Byline Bancorp with a short position of William Penn. Check out your portfolio center. Please also check ongoing floating volatility patterns of Byline Bancorp and William Penn.
Diversification Opportunities for Byline Bancorp and William Penn
0.81 | Correlation Coefficient |
Very poor diversification
The 3 months correlation between Byline and William is 0.81. Overlapping area represents the amount of risk that can be diversified away by holding Byline Bancorp and William Penn Bancorp in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on William Penn Bancorp and Byline 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 Byline Bancorp are associated (or correlated) with William Penn. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of William Penn Bancorp has no effect on the direction of Byline Bancorp i.e., Byline Bancorp and William Penn go up and down completely randomly.
Pair Corralation between Byline Bancorp and William Penn
Allowing for the 90-day total investment horizon Byline Bancorp is expected to generate 2.07 times more return on investment than William Penn. However, Byline Bancorp is 2.07 times more volatile than William Penn Bancorp. It trades about 0.09 of its potential returns per unit of risk. William Penn Bancorp is currently generating about 0.15 per unit of risk. If you would invest 2,733 in Byline Bancorp on September 3, 2024 and sell it today you would earn a total of 409.00 from holding Byline Bancorp or generate 14.97% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Strong |
Accuracy | 100.0% |
Values | Daily Returns |
Byline Bancorp vs. William Penn Bancorp
Performance |
Timeline |
Byline Bancorp |
William Penn Bancorp |
Byline Bancorp and William Penn Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Byline Bancorp and William Penn
The main advantage of trading using opposite Byline Bancorp and William Penn positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Byline Bancorp position performs unexpectedly, William Penn 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 William Penn will offset losses from the drop in William Penn's long position.Byline Bancorp vs. JPMorgan Chase Co | Byline Bancorp vs. Citigroup | Byline Bancorp vs. Wells Fargo | Byline Bancorp vs. Toronto Dominion Bank |
William Penn vs. JPMorgan Chase Co | William Penn vs. Citigroup | William Penn vs. Wells Fargo | William Penn vs. Toronto Dominion Bank |
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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