Data Communications Management Stock Technical Analysis
| DCM Stock | CAD 1.48 -0.01 -0.67% |
As of the 17th of March 2026, Data Communications is trading near 1.48 per share. Technical analytics identify Variance of 6.07, mean deviation of 1.81, and Standard Deviation of 2.46. The analytical framework assesses directional consistency across time frames. Peer-relative positioning is derived from normalized indicator data.
Data Communications Momentum Analysis
Momentum indicators are widely used technical indicators which help to measure the pace at which the price of specific equity, such as Data, fluctuates. Many momentum indicators also complement each other and can be helpful when the market is rising or falling as compared to DataData |
What if' Analysis
Backtesting a what-if scenario on Data Communications Management helps investors see how the stock may have behaved if the position had been entered, held, or resized under different historical assumptions. Current market capitalization is about 81.86 Million, enterprise value is near 338.92 Million, and annual revenue is around 450.36 Million. The point is not to predict the future from one chart, but to understand how sensitive the trade has been to timing and holding assumptions.
| 12/17/2025 |
| 03/17/2026 |
An initial 0.00 allocation to Data Communications on December 17, 2025 held through today would earn 0.00 in total gains. The change equals a 0.0% return on investment in Data Communications in total over a 90 day window. Data Communications has comparable peers such as DynaCERT, Titanium Transportation, BluMetric Environmental, Northstar Clean, Vitreous Glass, Atlas Engineered, and BioRem. The comparison helps frame competitive context. DATA Communications Management Corp. provides business communication solutions in North America More
Upside and Downside Indicators for Data Communications Dashboard
Upside/downside measures for Data Communications frame directional pressure and range behavior. The indicators are presented as neutral context for price dynamics.
| Information Ratio | -0.12 | |||
| Maximum Drawdown | 10.93 | |||
| Value At Risk | -4.02 | |||
| Potential Upside | 4.95 |
Market Risk Indicators for Data Communications Signals
This section presents risk metrics that describe Data Communications' historical price variability. This view provides neutral context for risk and variability.| Risk Adjusted Performance | -0.09 | |||
| Jensen Alpha | -0.33 | |||
| Total Risk Alpha | -0.24 | |||
| Treynor Ratio | 1.02 |
While mean reversion in Data Communications is a statistically observable tendency, it operates on uncertain timelines. Positions sized too aggressively against the trend can suffer sustained losses before reversion occurs.
Technical Indicators
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| Risk Adjusted Performance | -0.09 | |||
| Market Risk Adjusted Performance | 1.03 | |||
| Mean Deviation | 1.81 | |||
| Coefficient Of Variation | -800.62 | |||
| Standard Deviation | 2.46 | |||
| Variance | 6.07 | |||
| Information Ratio | -0.12 | |||
| Jensen Alpha | -0.33 | |||
| Total Risk Alpha | -0.24 | |||
| Treynor Ratio | 1.02 | |||
| Maximum Drawdown | 10.93 | |||
| Value At Risk | -4.02 | |||
| Potential Upside | 4.95 | |||
| Skewness | 0.3815 | |||
| Kurtosis | 0.4019 |
Data Communications Backtested Returns
Data Communications continues to exhibit a very high risk exposure over the designated horizon. It has a Sharpe Ratio of -0.0959, which indicates that -0.0959 units of return per unit of risk over the last 3 months. We identified twenty-two technical indicators supporting this volatility profile. Please examine metrics such as Variance of 6.07, mean deviation of 1.81, and standard deviation of 2.46 to validate volatility assumptions. The company owns a Beta (Systematic Risk) of -0.31, which signifies possible diversification benefits within a given portfolio. Data Communications shows a mild inverse relationship with the market, drifting lower in rallies and holding up during downturns. At this point, Data Communications has a negative expected return of -0.23%. Please make sure to confirm Data Communications' accumulation distribution, and the relationship between the potential upside and day median price, to decide if Data Communications's performance from the past will be repeated in the future.
Auto-correlation | -0.07 |
Very weak reverse predictability
Data Communications Management shows very weak reverse predictability when comparing price series from 17th of December 2025 to 31st of January 2026 against from 31st of January 2026 to 17th of March 2026. A strong serial relationship would imply that Data Communications's recent trajectory contains information about its near-term direction. With a serial correlation of -0.07, barely 7.0% of Data Communications's price variation is attributable to patterns in preceding intervals. Given that Data Communications Management has negative autocorrelation for the selected time horizon, market participants may evaluate potential contrarian price behavior over comparable future intervals.
| Correlation Coefficient | -0.07 | |
| Spearman Rank Test | -0.28 | |
| Residual Average | 0.0 | |
| Price Variance | 0.0 |
Technical analysis for Data Communications evaluates price and volume patterns over time. The analysis highlights moving averages, RSI, and price correlation signals across the stock cycle.
Technical Analysis
This analysis covers twenty-seven data points across the selected time horizon. The Average True Range was developed by J. Welles Wilder in 1970s. It is one of components of the Welles Wilder Directional Movement indicators. The ATR is a measure of Data Communications volatility. High ATR values indicate high volatility, and low values indicate low volatility.
Technical Analysis Methodology & Indicators
Technical analysis of Data Communications evaluates price structure, momentum, and volatility clustering. Price movements may be comparatively less responsive to macroeconomic volatility. Data Communications has a market cap of 81.86 M, P/E of 312.5, ROE of 23.91%.
Data shown for Data Communications Management is aggregated from periodic company reporting and market reference feeds and normalized across reporting formats. Source publication cadence can introduce delays.
This content is curated and reviewed by:
Raphi Shpitalnik - Junior Member of Macroaxis Editorial BoardData Communications Technical Indicators
Investors following Data Communications Management often rely on technical indicators to test whether price action is supporting continuation, exhaustion, or a possible change in direction. The stronger process confirms one signal with others instead of reacting to one pattern in isolation.
| Cycle Indicators | ||
| Math Operators | ||
| Math Transform | ||
| Momentum Indicators | ||
| Overlap Studies | ||
| Pattern Recognition | ||
| Price Transform | ||
| Statistic Functions | ||
| Volatility Indicators | ||
| Volume Indicators |
| Risk Adjusted Performance | -0.09 | |||
| Market Risk Adjusted Performance | 1.03 | |||
| Mean Deviation | 1.81 | |||
| Coefficient Of Variation | -800.62 | |||
| Standard Deviation | 2.46 | |||
| Variance | 6.07 | |||
| Information Ratio | -0.12 | |||
| Jensen Alpha | -0.33 | |||
| Total Risk Alpha | -0.24 | |||
| Treynor Ratio | 1.02 | |||
| Maximum Drawdown | 10.93 | |||
| Value At Risk | -4.02 | |||
| Potential Upside | 4.95 | |||
| Skewness | 0.3815 | |||
| Kurtosis | 0.4019 |
March 17, 2026 Daily Trend Indicators
Investors following Data Communications Management often rely on technical indicators to test whether price action is supporting continuation, exhaustion, or a possible change in direction. The stronger process confirms one signal with others instead of reacting to one pattern in isolation.
| Accumulation Distribution | 7,095 | ||
| Daily Balance Of Power | -0.09 | ||
| Rate Of Daily Change | 0.99 | ||
| Day Median Price | 1.50 | ||
| Day Typical Price | 1.49 | ||
| Price Action Indicator | -0.02 |
More Resources for Data Stock Analysis
Other Information on Investing in Data Stock
Financial ratios for Data Communications provide valuation context across profits, cash flow, and enterprise value. They help compare Data across measures in a consistent way.