Correlation Between Everyday People and First National
Can any of the company-specific risk be diversified away by investing in both Everyday People and First National 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 Everyday People and First National into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Everyday People Financial and First National Financial, you can compare the effects of market volatilities on Everyday People and First National 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 Everyday People with a short position of First National. Check out your portfolio center. Please also check ongoing floating volatility patterns of Everyday People and First National.
Diversification Opportunities for Everyday People and First National
0.34 | Correlation Coefficient |
Weak diversification
The 3 months correlation between Everyday and First is 0.34. Overlapping area represents the amount of risk that can be diversified away by holding Everyday People Financial and First National Financial in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on First National Financial and Everyday People 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 Everyday People Financial are associated (or correlated) with First National. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of First National Financial has no effect on the direction of Everyday People i.e., Everyday People and First National go up and down completely randomly.
Pair Corralation between Everyday People and First National
Assuming the 90 days horizon Everyday People Financial is expected to generate 5.44 times more return on investment than First National. However, Everyday People is 5.44 times more volatile than First National Financial. It trades about 0.03 of its potential returns per unit of risk. First National Financial is currently generating about 0.1 per unit of risk. If you would invest 35.00 in Everyday People Financial on August 26, 2024 and sell it today you would earn a total of 4.00 from holding Everyday People Financial or generate 11.43% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Very Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
Everyday People Financial vs. First National Financial
Performance |
| Timeline |
| Everyday People Financial |
| First National Financial |
Everyday People and First National Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Everyday People and First National
The main advantage of trading using opposite Everyday People and First National positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Everyday People position performs unexpectedly, First National 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 First National will offset losses from the drop in First National's long position.| Everyday People vs. NVIDIA CDR | Everyday People vs. Apple Inc CDR | Everyday People vs. Microsoft Corp CDR | Everyday People vs. Amazon CDR |
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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 Portfolio Rebalancing module to analyze risk-adjusted returns against different time horizons to find asset-allocation targets.
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