Correlation Between Dicker Data and Aristocrat Leisure
Can any of the company-specific risk be diversified away by investing in both Dicker Data and Aristocrat Leisure 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 Aristocrat Leisure into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Dicker Data and Aristocrat Leisure, you can compare the effects of market volatilities on Dicker Data and Aristocrat Leisure 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 Aristocrat Leisure. Check out your portfolio center. Please also check ongoing floating volatility patterns of Dicker Data and Aristocrat Leisure.
Diversification Opportunities for Dicker Data and Aristocrat Leisure
-0.6 | Correlation Coefficient |
Excellent diversification
The 3 months correlation between Dicker and Aristocrat is -0.6. Overlapping area represents the amount of risk that can be diversified away by holding Dicker Data and Aristocrat Leisure in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Aristocrat Leisure 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 Aristocrat Leisure. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Aristocrat Leisure has no effect on the direction of Dicker Data i.e., Dicker Data and Aristocrat Leisure go up and down completely randomly.
Pair Corralation between Dicker Data and Aristocrat Leisure
Assuming the 90 days trading horizon Dicker Data is expected to generate 60.72 times less return on investment than Aristocrat Leisure. In addition to that, Dicker Data is 1.49 times more volatile than Aristocrat Leisure. It trades about 0.0 of its total potential returns per unit of risk. Aristocrat Leisure is currently generating about 0.43 per unit of volatility. If you would invest 6,114 in Aristocrat Leisure on September 1, 2024 and sell it today you would earn a total of 661.00 from holding Aristocrat Leisure or generate 10.81% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
Dicker Data vs. Aristocrat Leisure
Performance |
| Timeline |
| Dicker Data |
| Aristocrat Leisure |
Dicker Data and Aristocrat Leisure Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Dicker Data and Aristocrat Leisure
The main advantage of trading using opposite Dicker Data and Aristocrat Leisure positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Dicker Data position performs unexpectedly, Aristocrat Leisure 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 Aristocrat Leisure will offset losses from the drop in Aristocrat Leisure's long position.| Dicker Data vs. K2 Asset Management | Dicker Data vs. Homeco Daily Needs | Dicker Data vs. Home Consortium | Dicker Data vs. Alternative Investment Trust |
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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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