Correlation Between Allegheny Technologies and ServiceNow
Can any of the company-specific risk be diversified away by investing in both Allegheny Technologies and ServiceNow 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 Allegheny Technologies and ServiceNow into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Allegheny Technologies Incorporated and ServiceNow, you can compare the effects of market volatilities on Allegheny Technologies and ServiceNow 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 Allegheny Technologies with a short position of ServiceNow. Check out your portfolio center. Please also check ongoing floating volatility patterns of Allegheny Technologies and ServiceNow.
Diversification Opportunities for Allegheny Technologies and ServiceNow
-0.44 | Correlation Coefficient |
Very good diversification
The 3 months correlation between Allegheny and ServiceNow is -0.44. Overlapping area represents the amount of risk that can be diversified away by holding Allegheny Technologies Incorpo and ServiceNow in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on ServiceNow and Allegheny Technologies 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 Allegheny Technologies Incorporated are associated (or correlated) with ServiceNow. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of ServiceNow has no effect on the direction of Allegheny Technologies i.e., Allegheny Technologies and ServiceNow go up and down completely randomly.
Pair Corralation between Allegheny Technologies and ServiceNow
Considering the 90-day investment horizon Allegheny Technologies is expected to generate 1.26 times less return on investment than ServiceNow. In addition to that, Allegheny Technologies is 1.13 times more volatile than ServiceNow. It trades about 0.07 of its total potential returns per unit of risk. ServiceNow is currently generating about 0.11 per unit of volatility. If you would invest 40,064 in ServiceNow on September 5, 2024 and sell it today you would earn a total of 65,668 from holding ServiceNow or generate 163.91% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Very Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Allegheny Technologies Incorpo vs. ServiceNow
Performance |
Timeline |
Allegheny Technologies |
ServiceNow |
Allegheny Technologies and ServiceNow Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Allegheny Technologies and ServiceNow
The main advantage of trading using opposite Allegheny Technologies and ServiceNow positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Allegheny Technologies position performs unexpectedly, ServiceNow 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 ServiceNow will offset losses from the drop in ServiceNow's long position.Allegheny Technologies vs. Northwest Pipe | Allegheny Technologies vs. Insteel Industries | Allegheny Technologies vs. ESAB Corp | Allegheny Technologies vs. Gulf Island Fabrication |
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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 Rebalancing module to analyze risk-adjusted returns against different time horizons to find asset-allocation targets.
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