Correlation Between Angel Oak and Banking Fund
Can any of the company-specific risk be diversified away by investing in both Angel Oak and Banking Fund 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 Angel Oak and Banking Fund into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Angel Oak Financial and Banking Fund Class, you can compare the effects of market volatilities on Angel Oak and Banking Fund 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 Angel Oak with a short position of Banking Fund. Check out your portfolio center. Please also check ongoing floating volatility patterns of Angel Oak and Banking Fund.
Diversification Opportunities for Angel Oak and Banking Fund
0.64 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Angel and Banking is 0.64. Overlapping area represents the amount of risk that can be diversified away by holding Angel Oak Financial and Banking Fund Class in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Banking Fund Class and Angel Oak 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 Angel Oak Financial are associated (or correlated) with Banking Fund. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Banking Fund Class has no effect on the direction of Angel Oak i.e., Angel Oak and Banking Fund go up and down completely randomly.
Pair Corralation between Angel Oak and Banking Fund
Assuming the 90 days horizon Angel Oak is expected to generate 15.87 times less return on investment than Banking Fund. But when comparing it to its historical volatility, Angel Oak Financial is 10.73 times less risky than Banking Fund. It trades about 0.15 of its potential returns per unit of risk. Banking Fund Class is currently generating about 0.22 of returns per unit of risk over similar time horizon. If you would invest 8,988 in Banking Fund Class on September 1, 2024 and sell it today you would earn a total of 1,075 from holding Banking Fund Class or generate 11.96% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Significant |
| Accuracy | 95.45% |
| Values | Daily Returns |
Angel Oak Financial vs. Banking Fund Class
Performance |
| Timeline |
| Angel Oak Financial |
| Banking Fund Class |
Angel Oak and Banking Fund Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Angel Oak and Banking Fund
The main advantage of trading using opposite Angel Oak and Banking Fund positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Angel Oak position performs unexpectedly, Banking Fund 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 Banking Fund will offset losses from the drop in Banking Fund's long position.| Angel Oak vs. Vanguard Total Stock | Angel Oak vs. Vanguard 500 Index | Angel Oak vs. Vanguard Total Stock | Angel Oak vs. Vanguard Total Stock |
| Banking Fund vs. Federated Kaufmann Large | Banking Fund vs. Victory Strategic Allocation | Banking Fund vs. Enhanced Large Pany | Banking Fund vs. Jhancock Disciplined Value |
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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