Correlation Between AG Mortgage and New York
Can any of the company-specific risk be diversified away by investing in both AG Mortgage and New York 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 AG Mortgage and New York into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between AG Mortgage Investment and New York Mortgage, you can compare the effects of market volatilities on AG Mortgage and New York 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 AG Mortgage with a short position of New York. Check out your portfolio center. Please also check ongoing floating volatility patterns of AG Mortgage and New York.
Diversification Opportunities for AG Mortgage and New York
0.73 | Correlation Coefficient |
Poor diversification
The 3 months correlation between MITT-PB and New is 0.73. Overlapping area represents the amount of risk that can be diversified away by holding AG Mortgage Investment and New York Mortgage in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on New York Mortgage and AG Mortgage 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 AG Mortgage Investment are associated (or correlated) with New York. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of New York Mortgage has no effect on the direction of AG Mortgage i.e., AG Mortgage and New York go up and down completely randomly.
Pair Corralation between AG Mortgage and New York
Assuming the 90 days trading horizon AG Mortgage Investment is expected to generate 1.1 times more return on investment than New York. However, AG Mortgage is 1.1 times more volatile than New York Mortgage. It trades about 0.16 of its potential returns per unit of risk. New York Mortgage is currently generating about 0.15 per unit of risk. If you would invest 1,620 in AG Mortgage Investment on August 27, 2024 and sell it today you would earn a total of 600.00 from holding AG Mortgage Investment or generate 37.04% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Significant |
| Accuracy | 100.0% |
| Values | Daily Returns |
AG Mortgage Investment vs. New York Mortgage
Performance |
| Timeline |
| AG Mortgage Investment |
| New York Mortgage |
AG Mortgage and New York Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with AG Mortgage and New York
The main advantage of trading using opposite AG Mortgage and New York positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if AG Mortgage position performs unexpectedly, New York 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 New York will offset losses from the drop in New York's long position.| AG Mortgage vs. New York Mortgage | AG Mortgage vs. New York Mortgage | AG Mortgage vs. Two Harbors Investment | AG Mortgage vs. Two Harbors Investment |
| New York vs. New York Mortgage | New York vs. New York Mortgage | New York vs. New York Mortgage | New York vs. AGNC Investment Corp |
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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