Correlation Between Dicker Data and Charter Hall
Can any of the company-specific risk be diversified away by investing in both Dicker Data and Charter Hall 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 Charter Hall into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Dicker Data and Charter Hall Retail, you can compare the effects of market volatilities on Dicker Data and Charter Hall 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 Charter Hall. Check out your portfolio center. Please also check ongoing floating volatility patterns of Dicker Data and Charter Hall.
Diversification Opportunities for Dicker Data and Charter Hall
0.53 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between Dicker and Charter is 0.53. Overlapping area represents the amount of risk that can be diversified away by holding Dicker Data and Charter Hall Retail in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Charter Hall Retail 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 are associated (or correlated) with Charter Hall. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Charter Hall Retail has no effect on the direction of Dicker Data i.e., Dicker Data and Charter Hall go up and down completely randomly.
Pair Corralation between Dicker Data and Charter Hall
Assuming the 90 days trading horizon Dicker Data is expected to under-perform the Charter Hall. In addition to that, Dicker Data is 1.32 times more volatile than Charter Hall Retail. It trades about -0.08 of its total potential returns per unit of risk. Charter Hall Retail is currently generating about 0.02 per unit of volatility. If you would invest 343.00 in Charter Hall Retail on August 29, 2024 and sell it today you would earn a total of 1.00 from holding Charter Hall Retail or generate 0.29% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
Dicker Data vs. Charter Hall Retail
Performance |
| Timeline |
| Dicker Data |
| Charter Hall Retail |
Dicker Data and Charter Hall Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Dicker Data and Charter Hall
The main advantage of trading using opposite Dicker Data and Charter Hall positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Dicker Data position performs unexpectedly, Charter Hall 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 Charter Hall will offset losses from the drop in Charter Hall's long position.| Dicker Data vs. Australian Unity Office | Dicker Data vs. Phoslock Environmental Technologies | Dicker Data vs. Vulcan Steel | Dicker Data vs. Medical Developments International |
| Charter Hall vs. Cromwell Property Group | Charter Hall vs. GDI Property Group | Charter Hall vs. Australian Unity Office |
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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