Correlation Between First Community and ESSA Bancorp
Can any of the company-specific risk be diversified away by investing in both First Community and ESSA 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 First Community and ESSA Bancorp into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between First Community and ESSA Bancorp, you can compare the effects of market volatilities on First Community and ESSA 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 First Community with a short position of ESSA Bancorp. Check out your portfolio center. Please also check ongoing floating volatility patterns of First Community and ESSA Bancorp.
Diversification Opportunities for First Community and ESSA Bancorp
0.78 | Correlation Coefficient |
Poor diversification
The 3 months correlation between First and ESSA is 0.78. Overlapping area represents the amount of risk that can be diversified away by holding First Community and ESSA Bancorp in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on ESSA Bancorp and First Community 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 First Community are associated (or correlated) with ESSA Bancorp. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of ESSA Bancorp has no effect on the direction of First Community i.e., First Community and ESSA Bancorp go up and down completely randomly.
Pair Corralation between First Community and ESSA Bancorp
Given the investment horizon of 90 days First Community is expected to generate 0.66 times more return on investment than ESSA Bancorp. However, First Community is 1.52 times less risky than ESSA Bancorp. It trades about 0.24 of its potential returns per unit of risk. ESSA Bancorp is currently generating about 0.02 per unit of risk. If you would invest 2,275 in First Community on August 24, 2024 and sell it today you would earn a total of 219.00 from holding First Community or generate 9.63% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Significant |
| Accuracy | 100.0% |
| Values | Daily Returns |
First Community vs. ESSA Bancorp
Performance |
| Timeline |
| First Community |
| ESSA Bancorp |
First Community and ESSA Bancorp Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with First Community and ESSA Bancorp
The main advantage of trading using opposite First Community and ESSA Bancorp positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if First Community position performs unexpectedly, ESSA 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 ESSA Bancorp will offset losses from the drop in ESSA Bancorp's long position.| First Community vs. Community West Bancshares | First Community vs. First Financial Northwest | First Community vs. First Northwest Bancorp | First Community vs. Home Federal Bancorp |
| ESSA Bancorp vs. Community West Bancshares | ESSA Bancorp vs. First Northwest Bancorp | ESSA Bancorp vs. First Community | ESSA Bancorp vs. Oak Valley 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 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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