Correlation Between DICKER DATA and PUBLIC STORAGE
Can any of the company-specific risk be diversified away by investing in both DICKER DATA and PUBLIC STORAGE 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 DICKER DATA and PUBLIC STORAGE into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between DICKER DATA LTD and PUBLIC STORAGE PRFO, you can compare the effects of market volatilities on DICKER DATA and PUBLIC STORAGE 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 DICKER DATA with a short position of PUBLIC STORAGE. Check out your portfolio center. Please also check ongoing floating volatility patterns of DICKER DATA and PUBLIC STORAGE.
Diversification Opportunities for DICKER DATA and PUBLIC STORAGE
0.23 | Correlation Coefficient |
Modest diversification
The 3 months correlation between DICKER and PUBLIC is 0.23. Overlapping area represents the amount of risk that can be diversified away by holding DICKER DATA LTD and PUBLIC STORAGE PRFO in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on PUBLIC STORAGE PRFO and DICKER DATA 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 DICKER DATA LTD are associated (or correlated) with PUBLIC STORAGE. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of PUBLIC STORAGE PRFO has no effect on the direction of DICKER DATA i.e., DICKER DATA and PUBLIC STORAGE go up and down completely randomly.
Pair Corralation between DICKER DATA and PUBLIC STORAGE
Assuming the 90 days horizon DICKER DATA LTD is expected to under-perform the PUBLIC STORAGE. In addition to that, DICKER DATA is 1.73 times more volatile than PUBLIC STORAGE PRFO. It trades about -0.04 of its total potential returns per unit of risk. PUBLIC STORAGE PRFO is currently generating about 0.08 per unit of volatility. 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 |
DICKER DATA LTD vs. PUBLIC STORAGE PRFO
Performance |
| Timeline |
| DICKER DATA LTD |
| PUBLIC STORAGE PRFO |
DICKER DATA and PUBLIC STORAGE Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with DICKER DATA and PUBLIC STORAGE
The main advantage of trading using opposite DICKER DATA and PUBLIC STORAGE positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if DICKER DATA position performs unexpectedly, PUBLIC STORAGE 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 PUBLIC STORAGE will offset losses from the drop in PUBLIC STORAGE's long position.| DICKER DATA vs. Chongqing Machinery Electric | DICKER DATA vs. Australian Agricultural | DICKER DATA vs. ALGOMA STEEL GROUP | DICKER DATA vs. Caltagirone SpA |
| PUBLIC STORAGE vs. Lyxor 1 | PUBLIC STORAGE vs. Xtrackers ShortDAX | PUBLIC STORAGE vs. Xtrackers LevDAX | PUBLIC STORAGE vs. Superior Plus 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 Equity Forecasting module to use basic forecasting models to generate price predictions and determine price momentum.
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