Correlation Between New York and Annaly Capital
Can any of the company-specific risk be diversified away by investing in both New York and Annaly Capital 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 New York and Annaly Capital into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between New York Mortgage and Annaly Capital Management, you can compare the effects of market volatilities on New York and Annaly Capital 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 New York with a short position of Annaly Capital. Check out your portfolio center. Please also check ongoing floating volatility patterns of New York and Annaly Capital.
Diversification Opportunities for New York and Annaly Capital
0.85 | Correlation Coefficient |
Very poor diversification
The 3 months correlation between New and Annaly is 0.85. Overlapping area represents the amount of risk that can be diversified away by holding New York Mortgage and Annaly Capital Management in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Annaly Capital Management and New York 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 New York Mortgage are associated (or correlated) with Annaly Capital. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Annaly Capital Management has no effect on the direction of New York i.e., New York and Annaly Capital go up and down completely randomly.
Pair Corralation between New York and Annaly Capital
Assuming the 90 days horizon New York Mortgage is expected to generate 3.29 times more return on investment than Annaly Capital. However, New York is 3.29 times more volatile than Annaly Capital Management. It trades about 0.09 of its potential returns per unit of risk. Annaly Capital Management is currently generating about 0.16 per unit of risk. If you would invest 2,105 in New York Mortgage on October 15, 2024 and sell it today you would earn a total of 98.00 from holding New York Mortgage or generate 4.66% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Strong |
Accuracy | 100.0% |
Values | Daily Returns |
New York Mortgage vs. Annaly Capital Management
Performance |
Timeline |
New York Mortgage |
Annaly Capital Management |
New York and Annaly Capital Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with New York and Annaly Capital
The main advantage of trading using opposite New York and Annaly Capital positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if New York position performs unexpectedly, Annaly Capital 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 Annaly Capital will offset losses from the drop in Annaly Capital's long position.New York vs. New York Mortgage | New York vs. New York Mortgage | New York vs. New York Mortgage | New York vs. AGNC Investment Corp |
Annaly Capital vs. AGNC Investment Corp | Annaly Capital vs. Annaly Capital Management | Annaly Capital vs. AGNC Investment Corp | Annaly Capital vs. New York Mortgage |
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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