Correlation Between Farmers Bancorp and Cisco Systems
Can any of the company-specific risk be diversified away by investing in both Farmers Bancorp and Cisco Systems 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 Farmers Bancorp and Cisco Systems into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Farmers Bancorp and Cisco Systems, you can compare the effects of market volatilities on Farmers Bancorp and Cisco Systems 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 Farmers Bancorp with a short position of Cisco Systems. Check out your portfolio center. Please also check ongoing floating volatility patterns of Farmers Bancorp and Cisco Systems.
Diversification Opportunities for Farmers Bancorp and Cisco Systems
0.54 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between Farmers and Cisco is 0.54. Overlapping area represents the amount of risk that can be diversified away by holding Farmers Bancorp and Cisco Systems in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Cisco Systems and Farmers 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 Farmers Bancorp are associated (or correlated) with Cisco Systems. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Cisco Systems has no effect on the direction of Farmers Bancorp i.e., Farmers Bancorp and Cisco Systems go up and down completely randomly.
Pair Corralation between Farmers Bancorp and Cisco Systems
Given the investment horizon of 90 days Farmers Bancorp is expected to generate 2.14 times more return on investment than Cisco Systems. However, Farmers Bancorp is 2.14 times more volatile than Cisco Systems. It trades about 0.13 of its potential returns per unit of risk. Cisco Systems is currently generating about 0.08 per unit of risk. If you would invest 3,299 in Farmers Bancorp on October 7, 2024 and sell it today you would earn a total of 301.00 from holding Farmers Bancorp or generate 9.12% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Farmers Bancorp vs. Cisco Systems
Performance |
Timeline |
Farmers Bancorp |
Cisco Systems |
Farmers Bancorp and Cisco Systems Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Farmers Bancorp and Cisco Systems
The main advantage of trading using opposite Farmers Bancorp and Cisco Systems positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Farmers Bancorp position performs unexpectedly, Cisco Systems 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 Cisco Systems will offset losses from the drop in Cisco Systems' long position.Farmers Bancorp vs. First Farmers Financial | Farmers Bancorp vs. Farmers Merchants Bancorp | Farmers Bancorp vs. Lakeland Financial | Farmers Bancorp vs. Eagle Financial Services |
Cisco Systems vs. KVH Industries | Cisco Systems vs. Knowles Cor | Cisco Systems vs. Comtech Telecommunications Corp | Cisco Systems vs. Lantronix |
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 Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.
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