Data Cost Of Revenue from 2010 to 2024

DCM Stock  CAD 2.12  0.03  1.40%   
Data Communications Cost Of Revenue yearly trend continues to be very stable with very little volatility. Cost Of Revenue is likely to drop to about 186.5 M. During the period from 2010 to 2024, Data Communications Cost Of Revenue quarterly data regression pattern had sample variance of 1727.9 T and median of  220,138,000. View All Fundamentals
 
Cost Of Revenue  
First Reported
2005-09-30
Previous Quarter
91.4 M
Current Value
80.7 M
Quarterly Volatility
13 M
 
Housing Crash
 
Credit Downgrade
 
Yuan Drop
 
Covid
Check Data Communications financial statements over time to gain insight into future company performance. You can evaluate financial statements to find patterns among Data Communications' main balance sheet or income statement drivers, such as Depreciation And Amortization of 22.8 M, Interest Expense of 16.1 M or Selling General Administrative of 50.5 M, as well as many indicators such as Price To Sales Ratio of 0.26, Dividend Yield of 0.56 or PTB Ratio of 4.86. Data financial statements analysis is a perfect complement when working with Data Communications Valuation or Volatility modules.
  
This module can also supplement various Data Communications Technical models . Check out the analysis of Data Communications Correlation against competitors.

Pair Trading with Data Communications

One of the main advantages of trading using pair correlations is that every trade hedges away some risk. Because there are two separate transactions required, even if Data Communications position performs unexpectedly, the other equity 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 Data Communications will appreciate offsetting losses from the drop in the long position's value.

Moving together with Data Stock

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Moving against Data Stock

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The ability to find closely correlated positions to Data Communications could be a great tool in your tax-loss harvesting strategies, allowing investors a quick way to find a similar-enough asset to replace Data Communications when you sell it. If you don't do this, your portfolio allocation will be skewed against your target asset allocation. So, investors can't just sell and buy back Data Communications - that would be a violation of the tax code under the "wash sale" rule, and this is why you need to find a similar enough asset and use the proceeds from selling Data Communications Management to buy it.
The correlation of Data Communications is a statistical measure of how it moves in relation to other instruments. This measure is expressed in what is known as the correlation coefficient, which ranges between -1 and +1. A perfect positive correlation (i.e., a correlation coefficient of +1) implies that as Data Communications moves, either up or down, the other security will move in the same direction. Alternatively, perfect negative correlation means that if Data Communications moves in either direction, the perfectly negatively correlated security will move in the opposite direction. If the correlation is 0, the equities are not correlated; they are entirely random. A correlation greater than 0.8 is generally described as strong, whereas a correlation less than 0.5 is generally considered weak.
Correlation analysis and pair trading evaluation for Data Communications can also be used as hedging techniques within a particular sector or industry or even over random equities to generate a better risk-adjusted return on your portfolios.
Pair CorrelationCorrelation Matching

Other Information on Investing in Data Stock

Data Communications financial ratios help investors to determine whether Data Stock is cheap or expensive when compared to a particular measure, such as profits or enterprise value. In other words, they help investors to determine the cost of investment in Data with respect to the benefits of owning Data Communications security.