Correlation Between Empire State and New York
Can any of the company-specific risk be diversified away by investing in both Empire State 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 Empire State and New York into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Empire State Realty and New York Community, you can compare the effects of market volatilities on Empire State 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 Empire State with a short position of New York. Check out your portfolio center. Please also check ongoing floating volatility patterns of Empire State and New York.
Diversification Opportunities for Empire State and New York
0.64 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Empire and New is 0.64. Overlapping area represents the amount of risk that can be diversified away by holding Empire State Realty and New York Community in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on New York Community and Empire State 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 Empire State Realty 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 Community has no effect on the direction of Empire State i.e., Empire State and New York go up and down completely randomly.
Pair Corralation between Empire State and New York
Given the investment horizon of 90 days Empire State is expected to generate 1.59 times less return on investment than New York. In addition to that, Empire State is 1.09 times more volatile than New York Community. It trades about 0.09 of its total potential returns per unit of risk. New York Community is currently generating about 0.15 per unit of volatility. If you would invest 1,690 in New York Community on September 1, 2024 and sell it today you would earn a total of 472.00 from holding New York Community or generate 27.93% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Significant |
| Accuracy | 100.0% |
| Values | Daily Returns |
Empire State Realty vs. New York Community
Performance |
| Timeline |
| Empire State Realty |
| New York Community |
Empire State and New York Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Empire State and New York
The main advantage of trading using opposite Empire State and New York positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Empire State 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.| Empire State vs. Paramount Group | Empire State vs. Hudson Pacific Properties | Empire State vs. Equity Commonwealth | Empire State vs. Douglas Emmett |
| New York vs. Wintrust Financial Corp | New York vs. Pinnacle Financial Partners | New York vs. Associated Banc Corp | New York vs. WesBanco |
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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