Correlation Between Mid Penn and William Penn
Can any of the company-specific risk be diversified away by investing in both Mid Penn 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 Mid Penn and William Penn into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Mid Penn Bancorp and William Penn Bancorp, you can compare the effects of market volatilities on Mid Penn 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 Mid Penn with a short position of William Penn. Check out your portfolio center. Please also check ongoing floating volatility patterns of Mid Penn and William Penn.
Diversification Opportunities for Mid Penn and William Penn
0.95 | Correlation Coefficient |
Almost no diversification
The 3 months correlation between Mid and William is 0.95. Overlapping area represents the amount of risk that can be diversified away by holding Mid Penn 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 Mid Penn 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 Mid Penn 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 Mid Penn i.e., Mid Penn and William Penn go up and down completely randomly.
Pair Corralation between Mid Penn and William Penn
Considering the 90-day investment horizon Mid Penn Bancorp is expected to under-perform the William Penn. But the stock apears to be less risky and, when comparing its historical volatility, Mid Penn Bancorp is 1.03 times less risky than William Penn. The stock trades about -0.09 of its potential returns per unit of risk. The William Penn Bancorp is currently generating about -0.08 of returns per unit of risk over similar time horizon. If you would invest 1,199 in William Penn Bancorp on December 27, 2024 and sell it today you would lose (98.00) from holding William Penn Bancorp or give up 8.17% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Very Strong |
Accuracy | 100.0% |
Values | Daily Returns |
Mid Penn Bancorp vs. William Penn Bancorp
Performance |
Timeline |
Mid Penn Bancorp |
William Penn Bancorp |
Mid Penn and William Penn Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Mid Penn and William Penn
The main advantage of trading using opposite Mid Penn and William Penn positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Mid Penn 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.Mid Penn vs. Affinity Bancshares | Mid Penn vs. Home Federal Bancorp | Mid Penn vs. Franklin Financial Services | Mid Penn vs. ACNB Corporation |
William Penn vs. Home Bancorp | William Penn vs. Rhinebeck Bancorp | William Penn vs. LINKBANCORP | William Penn vs. Magyar Bancorp |
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 Headlines Timeline module to stay connected to all market stories and filter out noise. Drill down to analyze hype elasticity.
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