Automatic Data Stock Forward View - Simple Moving Average
| ADP Stock | EUR 182.50 1.06 0.58% |
This reference page presents Simple Moving Average forecast data for Automatic Data Processing. The projected values and error metrics are presented below as reference information. The output values and deviation metrics are provided for informational reference.
The Simple Moving Average forecasted value of Automatic Data Processing on the next trading day is expected to be 182.50 with a mean absolute deviation of 3.30 and the sum of the absolute errors of 194.79.The simple moving average model is conceptually a linear regression of the current value of Automatic Data Processing price series against current and previous (unobserved) value of Automatic Data. In time series analysis, the simple moving-average model is a very common approach for modeling univariate price series models including forecasting prices into the future This Simple Moving Average forecast data for Automatic Data Processing is sourced from the most recent available trading data and is intended solely as reference information. Simple Moving Average Price Forecast For the 24th of March
Given 90 days horizon, the Simple Moving Average forecasted value of Automatic Data Processing on the next trading day is expected to be 182.50 with a mean absolute deviation of 3.30 , mean absolute percentage error of 18.09 , and the sum of the absolute errors of 194.79 .Please note that although there have been many attempts to predict Automatic Stock prices using its time series forecasting, we generally do not suggest using it to place bets in the real market. The most commonly used models for forecasting predictions are the autoregressive models, which specify that Automatic Data's next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).
Stock Forecast Pattern
| Backtest Automatic Data | Automatic Data Price Prediction | Research Analysis |
Forecasted Value
This next-day forecast for Automatic Data Processing uses model performance to estimate practical downside and upside boundaries rather than a single point target alone. At the moment, the model places downside around 180.61 and upside around 184.39 for the forecasting period.
Model Predictive Factors
The below table displays some essential indicators generated by the model showing the Simple Moving Average forecasting method's relative quality and the estimations of the prediction error of Automatic Data stock data series using in forecasting. Note that when a statistical model is used to represent Automatic Data 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 | 117.3299 |
| Bias | Arithmetic mean of the errors | 0.9253 |
| MAD | Mean absolute deviation | 3.3016 |
| MAPE | Mean absolute percentage error | 0.017 |
| SAE | Sum of the absolute errors | 194.795 |
Other Forecasting Options for Automatic Data
Automatic Data's daily price returns can be decomposed into trend, seasonal, and residual components. Divergence between short-term and long-term averages in Automatic often signals an upcoming reversal or acceleration. Gap analysis of Automatic Stock data examines overnight jumps between Automatic Data's closing and opening prices.Automatic Data Related Equities
These stocks within the Industrials space are often compared to Automatic Data by analysts and fund managers in the sector. Return on equity across these peers shows how well each firm turns capital into profit. How Automatic Data ranks within this group can shift over time as the competitive picture changes.
| Risk & Return | Correlation |
Automatic Data Market Strength Events
Market strength indicators help investors evaluate how Automatic Data stock reacts to evolving market conditions. These indicators help determine optimal entry and exit points for trading Automatic Data Processing. These indicators can identify periods when trading Automatic Data Processing may offer more favorable risk-reward conditions.
Automatic Data Risk Indicators
The analysis of Automatic Data's basic risk indicators is one of the essential steps in accurately forecasting its future price. Understanding the risk involved in holding Automatic Data's allows investors to make informed decisions about their exposure. The analysis of Automatic Data's basic risk metrics provides a foundation for managing investment risk.
| Mean Deviation | 1.42 | |||
| Standard Deviation | 1.84 | |||
| Variance | 3.37 |
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.
Story Coverage note for Automatic Data
Coverage intensity for Automatic Data Processing matters because narrative visibility can influence sentiment, participation, and volatility around the name. A disciplined read of coverage separates durable relevance from temporary noise.
Other Macroaxis Stories
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More Resources for Automatic Stock Analysis
Understanding Automatic Data Processing starts with its core financial statements, trend data, and ratio analysis. The dataset reflects Automatic Data's available reporting history.The Historical Fundamental Analysis of Automatic Data module adds a historical reference layer for Automatic Data's projections. With Automatic Data showing P/E 28.24 and ROE 73.84%, investors get more value when this analysis is combined with the diversification and construction tools below. The market appears to be paying up for strong returns - checking whether growth justifies that premium matters. You can also try the Content Syndication module to quickly integrate customizable finance content to your own investment portal.