Correlation Between Public Storage and DALATA HOTEL
Can any of the company-specific risk be diversified away by investing in both Public Storage and DALATA HOTEL 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 Public Storage and DALATA HOTEL into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Public Storage and DALATA HOTEL, you can compare the effects of market volatilities on Public Storage and DALATA HOTEL 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 Public Storage with a short position of DALATA HOTEL. Check out your portfolio center. Please also check ongoing floating volatility patterns of Public Storage and DALATA HOTEL.
Diversification Opportunities for Public Storage and DALATA HOTEL
0.16 | Correlation Coefficient |
Average diversification
The 3 months correlation between Public and DALATA is 0.16. Overlapping area represents the amount of risk that can be diversified away by holding Public Storage and DALATA HOTEL in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on DALATA HOTEL and Public Storage 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 Public Storage are associated (or correlated) with DALATA HOTEL. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of DALATA HOTEL has no effect on the direction of Public Storage i.e., Public Storage and DALATA HOTEL go up and down completely randomly.
Pair Corralation between Public Storage and DALATA HOTEL
Assuming the 90 days horizon Public Storage is expected to generate 1.3 times less return on investment than DALATA HOTEL. But when comparing it to its historical volatility, Public Storage is 2.79 times less risky than DALATA HOTEL. It trades about 0.1 of its potential returns per unit of risk. DALATA HOTEL is currently generating about 0.05 of returns per unit of risk over similar time horizon. If you would invest 390.00 in DALATA HOTEL on September 5, 2024 and sell it today you would earn a total of 28.00 from holding DALATA HOTEL or generate 7.18% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Insignificant |
Accuracy | 98.46% |
Values | Daily Returns |
Public Storage vs. DALATA HOTEL
Performance |
Timeline |
Public Storage |
DALATA HOTEL |
Public Storage and DALATA HOTEL Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Public Storage and DALATA HOTEL
The main advantage of trading using opposite Public Storage and DALATA HOTEL positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Public Storage position performs unexpectedly, DALATA HOTEL 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 DALATA HOTEL will offset losses from the drop in DALATA HOTEL's long position.Public Storage vs. TYSNES SPAREBANK NK | Public Storage vs. CDN IMPERIAL BANK | Public Storage vs. VIRG NATL BANKSH | Public Storage vs. Chiba Bank |
DALATA HOTEL vs. SINGAPORE AIRLINES | DALATA HOTEL vs. QBE Insurance Group | DALATA HOTEL vs. CDN IMPERIAL BANK | DALATA HOTEL vs. PT Bank Maybank |
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 Equity Forecasting module to use basic forecasting models to generate price predictions and determine price momentum.
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