Correlation Between PUBLIC STORAGE and DICKER DATA
Can any of the company-specific risk be diversified away by investing in both PUBLIC STORAGE and DICKER DATA 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 DICKER DATA into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between PUBLIC STORAGE PRFO and DICKER DATA LTD, you can compare the effects of market volatilities on PUBLIC STORAGE and DICKER DATA 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 DICKER DATA. Check out your portfolio center. Please also check ongoing floating volatility patterns of PUBLIC STORAGE and DICKER DATA.
Diversification Opportunities for PUBLIC STORAGE and DICKER DATA
0.23 | Correlation Coefficient |
Modest diversification
The 3 months correlation between PUBLIC and DICKER is 0.23. Overlapping area represents the amount of risk that can be diversified away by holding PUBLIC STORAGE PRFO and DICKER DATA LTD in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on DICKER DATA LTD 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 PRFO are associated (or correlated) with DICKER DATA. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of DICKER DATA LTD has no effect on the direction of PUBLIC STORAGE i.e., PUBLIC STORAGE and DICKER DATA go up and down completely randomly.
Pair Corralation between PUBLIC STORAGE and DICKER DATA
Assuming the 90 days trading horizon PUBLIC STORAGE PRFO is expected to generate 0.58 times more return on investment than DICKER DATA. However, PUBLIC STORAGE PRFO is 1.73 times less risky than DICKER DATA. It trades about 0.08 of its potential returns per unit of risk. DICKER DATA LTD is currently generating about -0.04 per unit of risk. If you would invest 1,567 in PUBLIC STORAGE PRFO on September 2, 2024 and sell it today you would earn a total of 83.00 from holding PUBLIC STORAGE PRFO or generate 5.3% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Very Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
PUBLIC STORAGE PRFO vs. DICKER DATA LTD
Performance |
| Timeline |
| PUBLIC STORAGE PRFO |
| DICKER DATA LTD |
PUBLIC STORAGE and DICKER DATA Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with PUBLIC STORAGE and DICKER DATA
The main advantage of trading using opposite PUBLIC STORAGE and DICKER DATA positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if PUBLIC STORAGE position performs unexpectedly, DICKER DATA 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 DICKER DATA will offset losses from the drop in DICKER DATA's long position.| PUBLIC STORAGE vs. Lyxor 1 | PUBLIC STORAGE vs. Xtrackers ShortDAX | PUBLIC STORAGE vs. Xtrackers LevDAX | PUBLIC STORAGE vs. Superior Plus Corp |
| DICKER DATA vs. Chongqing Machinery Electric | DICKER DATA vs. Australian Agricultural | DICKER DATA vs. ALGOMA STEEL GROUP | DICKER DATA vs. Caltagirone SpA |
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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