Automatic Data Stock Forward View - Simple Moving Average
| ADP Stock | USD 211.83 1.17 0.56% |
The Simple Moving Average forecast reference data for Automatic Data Processing is based on the equity's recent trading history. This page summarizes the model output and key accuracy metrics for reference.
The Simple Moving Average forecasted value of Automatic Data Processing on the next trading day is expected to be 211.25 with a mean absolute deviation of 3.66 and the sum of the absolute errors of 216.19.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 All Simple Moving Average forecast figures shown for Automatic Data Processing are reference data reflecting model output based on available historical prices. Simple Moving Average Price Forecast For the 21st 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 211.25 with a mean absolute deviation of 3.66 , mean absolute percentage error of 22.15 , and the sum of the absolute errors of 216.19 .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
Forecasting Automatic Data Processing for the next session involves measuring the model's historical ability to define credible downside and upside scenarios. Investors should still remember that no empirical framework consistently proves that one family of forecasting models will outperform all other approaches in live markets.
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 | 117.5324 |
| Bias | Arithmetic mean of the errors | 1.1279 |
| MAD | Mean absolute deviation | 3.6643 |
| MAPE | Mean absolute percentage error | 0.0161 |
| SAE | Sum of the absolute errors | 216.195 |
Other Forecasting Options for Automatic Data
Whether a novice or experienced investor, anyone considering Automatic needs to understand the dynamics of Automatic Data's price movement. Price charts for Automatic Stock contain a significant amount of noise that can distort investment decisions.Automatic Data Related Equities
The following equities are related to Automatic Data within the Industrials space and can be used for peer comparison, relative valuation, or portfolio diversification. Comparing Automatic Data against peers on metrics such as P/E, margins, and return on equity helps contextualize its positioning and identify relative strengths or weaknesses.
| Risk & Return | Correlation |
Automatic Data Market Strength Events
Analyzing market strength indicators for Automatic Data enables investors to understand how the stock performs relative to overall market momentum. These indicators are valuable tools for identifying when to enter or exit positions in Automatic Data Processing.
Automatic Data Risk Indicators
Identifying and analyzing Automatic Data's key risk indicators is a foundational step in projecting how its price may evolve. This process quantifies the risk associated with Automatic Data's and decide how to manage it.
| Mean Deviation | 1.26 | |||
| Standard Deviation | 1.62 | |||
| Variance | 2.63 |
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. This is most useful when investors want to understand why a security is suddenly drawing more public discussion.
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Automatic Data Short Properties
Short sentiment tied to Automatic Data Processing matters because heavier bearish pressure can change how quickly future price expectations become unstable. The stronger read compares short sentiment with trend behavior, volume, and the broader market narrative.
| Common Stock Shares Outstanding | 408.7 M | |
| Cash And Short Term Investments | 7.8 B |
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