Correlation Between COSTCO WHOLESALE and Automatic Data
Can any of the company-specific risk be diversified away by investing in both COSTCO WHOLESALE and Automatic 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 COSTCO WHOLESALE and Automatic Data into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between COSTCO WHOLESALE CDR and Automatic Data Processing, you can compare the effects of market volatilities on COSTCO WHOLESALE and Automatic 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 COSTCO WHOLESALE with a short position of Automatic Data. Check out your portfolio center. Please also check ongoing floating volatility patterns of COSTCO WHOLESALE and Automatic Data.
Diversification Opportunities for COSTCO WHOLESALE and Automatic Data
0.8 | Correlation Coefficient |
Very poor diversification
The 3 months correlation between COSTCO and Automatic is 0.8. Overlapping area represents the amount of risk that can be diversified away by holding COSTCO WHOLESALE CDR and Automatic Data Processing in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Automatic Data Processing and COSTCO WHOLESALE 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 COSTCO WHOLESALE CDR are associated (or correlated) with Automatic Data. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Automatic Data Processing has no effect on the direction of COSTCO WHOLESALE i.e., COSTCO WHOLESALE and Automatic Data go up and down completely randomly.
Pair Corralation between COSTCO WHOLESALE and Automatic Data
Assuming the 90 days trading horizon COSTCO WHOLESALE CDR is expected to generate 1.21 times more return on investment than Automatic Data. However, COSTCO WHOLESALE is 1.21 times more volatile than Automatic Data Processing. It trades about 0.11 of its potential returns per unit of risk. Automatic Data Processing is currently generating about 0.09 per unit of risk. If you would invest 1,609 in COSTCO WHOLESALE CDR on September 4, 2024 and sell it today you would earn a total of 1,351 from holding COSTCO WHOLESALE CDR or generate 83.97% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Strong |
| Accuracy | 100.0% |
| Values | Daily Returns |
COSTCO WHOLESALE CDR vs. Automatic Data Processing
Performance |
| Timeline |
| COSTCO WHOLESALE CDR |
| Automatic Data Processing |
COSTCO WHOLESALE and Automatic Data Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with COSTCO WHOLESALE and Automatic Data
The main advantage of trading using opposite COSTCO WHOLESALE and Automatic Data positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if COSTCO WHOLESALE position performs unexpectedly, Automatic 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 Automatic Data will offset losses from the drop in Automatic Data's long position.| COSTCO WHOLESALE vs. Walmart | COSTCO WHOLESALE vs. Superior Plus Corp | COSTCO WHOLESALE vs. NMI Holdings | COSTCO WHOLESALE vs. Origin Agritech |
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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 Portfolio Optimization module to compute new portfolio that will generate highest expected return given your specified tolerance for risk.
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