Correlation Between Automatic Data and Lockheed Martin
Can any of the company-specific risk be diversified away by investing in both Automatic Data and Lockheed Martin 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 Lockheed Martin 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 Lockheed Martin, you can compare the effects of market volatilities on Automatic Data and Lockheed Martin 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 Lockheed Martin. Check out your portfolio center. Please also check ongoing floating volatility patterns of Automatic Data and Lockheed Martin.
Diversification Opportunities for Automatic Data and Lockheed Martin
0.2 | Correlation Coefficient |
Modest diversification
The 3 months correlation between Automatic and Lockheed is 0.2. Overlapping area represents the amount of risk that can be diversified away by holding Automatic Data Processing and Lockheed Martin in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Lockheed Martin 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 Lockheed Martin. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Lockheed Martin has no effect on the direction of Automatic Data i.e., Automatic Data and Lockheed Martin go up and down completely randomly.
Pair Corralation between Automatic Data and Lockheed Martin
Assuming the 90 days trading horizon Automatic Data Processing is expected to generate 0.73 times more return on investment than Lockheed Martin. However, Automatic Data Processing is 1.37 times less risky than Lockheed Martin. It trades about -0.07 of its potential returns per unit of risk. Lockheed Martin is currently generating about -0.13 per unit of risk. If you would invest 7,476 in Automatic Data Processing on December 26, 2024 and sell it today you would lose (448.00) from holding Automatic Data Processing or give up 5.99% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Very Weak |
Accuracy | 98.36% |
Values | Daily Returns |
Automatic Data Processing vs. Lockheed Martin
Performance |
Timeline |
Automatic Data Processing |
Lockheed Martin |
Risk-Adjusted Performance
Very Weak
Weak | Strong |
Automatic Data and Lockheed Martin Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Automatic Data and Lockheed Martin
The main advantage of trading using opposite Automatic Data and Lockheed Martin positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Automatic Data position performs unexpectedly, Lockheed Martin 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 Lockheed Martin will offset losses from the drop in Lockheed Martin's long position.Automatic Data vs. The Home Depot | Automatic Data vs. Bemobi Mobile Tech | Automatic Data vs. Clover Health Investments, | Automatic Data vs. Metalurgica Gerdau SA |
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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 Pattern Recognition module to use different Pattern Recognition models to time the market across multiple global exchanges.
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