Data Communications Stock Forecast - 4 Period Moving Average
| DCM Stock | CAD 1.89 0.12 6.78% |
The 4 Period Moving Average forecasted value of Data Communications Management on the next trading day is expected to be 1.82 with a mean absolute deviation of 0.09 and the sum of the absolute errors of 5.12. Data Stock Forecast is based on your current time horizon. Although Data Communications' naive historical forecasting may sometimes provide an important future outlook for the firm, we recommend always cross-verifying it against solid analysis of Data Communications' systematic risk associated with finding meaningful patterns of Data Communications fundamentals over time.
Data |
Data Communications 4 Period Moving Average Price Forecast For the 23rd of November
Given 90 days horizon, the 4 Period Moving Average forecasted value of Data Communications Management on the next trading day is expected to be 1.82 with a mean absolute deviation of 0.09, mean absolute percentage error of 0.03, and the sum of the absolute errors of 5.12.Please note that although there have been many attempts to predict Data Stock prices using its time series forecasting, we generally do not recommend using it to place bets in the real market. The most commonly used models for forecasting predictions are the autoregressive models, which specify that Data Communications' next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).
Data Communications Stock Forecast Pattern
| Backtest Data Communications | Data Communications Price Prediction | Buy or Sell Advice |
Data Communications Forecasted Value
In the context of forecasting Data Communications' Stock value on the next trading day, we examine the predictive performance of the model to find good statistically significant boundaries of downside and upside scenarios. Data Communications' downside and upside margins for the forecasting period are 0.02 and 6.13, respectively. We have considered Data Communications' daily market price to evaluate the above model's predictive performance. Remember, however, there is no scientific proof or empirical evidence that traditional linear or nonlinear forecasting models outperform artificial intelligence and frequency domain models to provide accurate forecasts consistently.
Model Predictive Factors
The below table displays some essential indicators generated by the model showing the 4 Period Moving Average forecasting method's relative quality and the estimations of the prediction error of Data Communications stock data series using in forecasting. Note that when a statistical model is used to represent Data Communications stock, the representation will rarely be exact; so some information will be lost using the model to explain the process. AIC estimates the relative amount of information lost by a given model: the less information a model loses, the higher its quality.| AIC | Akaike Information Criteria | 107.4025 |
| Bias | Arithmetic mean of the errors | 0.0508 |
| MAD | Mean absolute deviation | 0.0898 |
| MAPE | Mean absolute percentage error | 0.0406 |
| SAE | Sum of the absolute errors | 5.12 |
Predictive Modules for Data Communications
There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as Data Communications. Regardless of method or technology, however, to accurately forecast the stock market is more a matter of luck rather than a particular technique. Nevertheless, trying to predict the stock market accurately is still an essential part of the overall investment decision process. Using different forecasting techniques and comparing the results might improve your chances of accuracy even though unexpected events may often change the market sentiment and impact your forecasting results.Other Forecasting Options for Data Communications
For every potential investor in Data, whether a beginner or expert, Data Communications' price movement is the inherent factor that sparks whether it is viable to invest in it or hold it better. Data Stock price charts are filled with many 'noises.' These noises can hugely alter the decision one can make regarding investing in Data. Basic forecasting techniques help filter out the noise by identifying Data Communications' price trends.Data Communications Related Equities
One of the popular trading techniques among algorithmic traders is to use market-neutral strategies where every trade hedges away some risk. Because there are two separate transactions required, even if one position performs unexpectedly, the other equity can make up some of the losses. Below are some of the equities that can be combined with Data Communications stock to make a market-neutral strategy. Peer analysis of Data Communications could also be used in its relative valuation, which is a method of valuing Data Communications by comparing valuation metrics with similar companies.
| Risk & Return | Correlation |
Data Communications Technical and Predictive Analytics
The stock market is financially volatile. Despite the volatility, there exist limitless possibilities of gaining profits and building passive income portfolios. With the complexity of Data Communications' price movements, a comprehensive understanding of forecasting methods that an investor can rely on to make the right move is invaluable. These methods predict trends that assist an investor in predicting the movement of Data Communications' current price.| Cycle Indicators | ||
| Math Operators | ||
| Math Transform | ||
| Momentum Indicators | ||
| Overlap Studies | ||
| Pattern Recognition | ||
| Price Transform | ||
| Statistic Functions | ||
| Volatility Indicators | ||
| Volume Indicators |
Data Communications Market Strength Events
Market strength indicators help investors to evaluate how Data Communications stock reacts to ongoing and evolving market conditions. The investors can use it to make informed decisions about market timing, and determine when trading Data Communications shares will generate the highest return on investment. By undertsting and applying Data Communications stock market strength indicators, traders can identify Data Communications Management entry and exit signals to maximize returns.
| Accumulation Distribution | 19976.87 | |||
| Daily Balance Of Power | 1.090909 | |||
| Rate Of Daily Change | 1.07 | |||
| Day Median Price | 1.85 | |||
| Day Typical Price | 1.86 | |||
| Price Action Indicator | 0.1 | |||
| Period Momentum Indicator | 0.12 |
Data Communications Risk Indicators
The analysis of Data Communications' basic risk indicators is one of the essential steps in accurately forecasting its future price. The process involves identifying the amount of risk involved in Data Communications' investment and either accepting that risk or mitigating it. Along with some essential techniques for forecasting data stock prices, we also provide a set of basic risk indicators that can assist in the individual investment decision or help in hedging the risk of your existing portfolios.
| Mean Deviation | 1.83 | |||
| Standard Deviation | 4.27 | |||
| Variance | 18.23 |
Please note, the risk measures we provide can be used independently or collectively to perform a risk assessment. When comparing two potential investments, we recommend comparing similar equities with homogenous growth potential and valuation from related markets to determine which investment holds the most risk.
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 against Data Stock
| 0.73 | FFH-PM | Fairfax Financial | PairCorr |
| 0.72 | FFH | Fairfax Financial | PairCorr |
| 0.7 | FFH-PD | Fairfax Financial | PairCorr |
| 0.62 | FFH-PE | Fairfax Financial | PairCorr |
| 0.61 | GS | GOLDMAN SACHS CDR | PairCorr |
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.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.