Data Communications Stock Forecast - Triple Exponential Smoothing
| DCM Stock | CAD 2.67 0.08 2.91% |
The Triple Exponential Smoothing forecasted value of Data Communications Management on the next trading day is expected to be 2.65 with a mean absolute deviation of 0.07 and the sum of the absolute errors of 4.17. 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 |
Most investors in Data Communications cannot accurately predict what will happen the next trading day because, historically, stock markets tend to be unpredictable and even illogical. Modeling turbulent structures requires applying different statistical methods, techniques, and algorithms to find hidden data structures or patterns within the Data Communications' time series price data and predict how it will affect future prices. One of these methodologies is forecasting, which interprets Data Communications' price structures and extracts relationships that further increase the generated results' accuracy.
Triple exponential smoothing for Data Communications - also known as the Winters method - is a refinement of the popular double exponential smoothing model with the addition of periodicity (seasonality) component. Simple exponential smoothing technique works best with data where there are no trend or seasonality components to the data. When Data Communications prices exhibit either an increasing or decreasing trend over time, simple exponential smoothing forecasts tend to lag behind observations. Double exponential smoothing is designed to address this type of data series by taking into account any trend in Data Communications price movement. However, neither of these exponential smoothing models address any seasonality of Data Communications. Data Communications Triple Exponential Smoothing Price Forecast For the 19th of June
Given 90 days horizon, the Triple Exponential Smoothing forecasted value of Data Communications Management on the next trading day is expected to be 2.65 with a mean absolute deviation of 0.07, mean absolute percentage error of 0.01, and the sum of the absolute errors of 4.17.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 0.03 and 5.54, 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 Triple Exponential Smoothing 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 | Huge |
| Bias | Arithmetic mean of the errors | 0.0158 |
| MAD | Mean absolute deviation | 0.0707 |
| MAPE | Mean absolute percentage error | 0.0227 |
| SAE | Sum of the absolute errors | 4.1726 |
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.Sophisticated investors, who have witnessed many market ups and downs, anticipate that the market will even out over time. This tendency of Data Communications' price to converge to an average value over time is called mean reversion. However, historically, high market prices usually discourage investors that believe in mean reversion to invest, while low prices are viewed as an opportunity to buy.
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.
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 | 2.15 | |||
| Standard Deviation | 3.06 | |||
| Variance | 9.38 |
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.
Building efficient market-beating portfolios requires time, education, and a lot of computing power!
The Portfolio Architect is an AI-driven system that provides multiple benefits to our users by leveraging cutting-edge machine learning algorithms, statistical analysis, and predictive modeling to automate the process of asset selection and portfolio construction, saving time and reducing human error for individual and institutional investors.
Try AI Portfolio ArchitectOther 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.