Correlation Between Automatic Data and Kanzhun
Can any of the company-specific risk be diversified away by investing in both Automatic Data and Kanzhun 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 Automatic Data and Kanzhun into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Automatic Data Processing and Kanzhun Ltd ADR, you can compare the effects of market volatilities on Automatic Data and Kanzhun 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 Automatic Data with a short position of Kanzhun. Check out your portfolio center. Please also check ongoing floating volatility patterns of Automatic Data and Kanzhun.
Diversification Opportunities for Automatic Data and Kanzhun
0.18 | Correlation Coefficient |
Average diversification
The 3 months correlation between Automatic and Kanzhun is 0.18. Overlapping area represents the amount of risk that can be diversified away by holding Automatic Data Processing and Kanzhun Ltd ADR in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Kanzhun Ltd ADR and Automatic 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 Automatic Data Processing are associated (or correlated) with Kanzhun. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Kanzhun Ltd ADR has no effect on the direction of Automatic Data i.e., Automatic Data and Kanzhun go up and down completely randomly.
Pair Corralation between Automatic Data and Kanzhun
Considering the 90-day investment horizon Automatic Data Processing is expected to generate 0.61 times more return on investment than Kanzhun. However, Automatic Data Processing is 1.63 times less risky than Kanzhun. It trades about 0.23 of its potential returns per unit of risk. Kanzhun Ltd ADR is currently generating about -0.38 per unit of risk. If you would invest 28,644 in Automatic Data Processing on August 27, 2024 and sell it today you would earn a total of 1,871 from holding Automatic Data Processing or generate 6.53% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Insignificant |
| Accuracy | 100.0% |
| Values | Daily Returns |
Automatic Data Processing vs. Kanzhun Ltd ADR
Performance |
| Timeline |
| Automatic Data Processing |
| Kanzhun Ltd ADR |
Automatic Data and Kanzhun Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Automatic Data and Kanzhun
The main advantage of trading using opposite Automatic Data and Kanzhun positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Automatic Data position performs unexpectedly, Kanzhun 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 Kanzhun will offset losses from the drop in Kanzhun's long position.| Automatic Data vs. Robert Half International | Automatic Data vs. Barrett Business Services | Automatic Data vs. ManpowerGroup | Automatic Data vs. Kforce Inc |
| Kanzhun vs. Ziprecruiter | Kanzhun vs. Automatic Data Processing | Kanzhun vs. Robert Half International | Kanzhun vs. TrueBlue |
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 Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.
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