Automatic Data Stock Forecast - Polynomial Regression
| ADP Stock | 228.50 0.30 0.13% |
The Polynomial Regression forecasted value of Automatic Data Processing on the next trading day is expected to be 225.84 with a mean absolute deviation of 3.15 and the sum of the absolute errors of 192.22. Automatic Stock Forecast is based on your current time horizon.
At this time the value of rsi of Automatic Data's share price is below 20 . This suggests that the stock is significantly oversold. The fundamental principle of the Relative Strength Index (RSI) is to quantify the velocity at which market participants are driving the price of a financial instrument upwards or downwards. Momentum 0
Sell Peaked
Oversold | Overbought |
Using Automatic Data hype-based prediction, you can estimate the value of Automatic Data Processing from the perspective of Automatic Data response to recently generated media hype and the effects of current headlines on its competitors.
The Polynomial Regression forecasted value of Automatic Data Processing on the next trading day is expected to be 225.84 with a mean absolute deviation of 3.15 and the sum of the absolute errors of 192.22. Automatic Data after-hype prediction price | EUR 228.5 |
There is no one specific way to measure market sentiment using hype analysis or a similar predictive technique. This prediction method should be used in combination with more fundamental and traditional techniques such as stock price forecasting, technical analysis, analysts consensus, earnings estimates, and various momentum models.
Automatic |
Automatic Data Additional Predictive Modules
Most predictive techniques to examine Automatic price help traders to determine how to time the market. We provide a combination of tools to recognize potential entry and exit points for Automatic using various technical indicators. When you analyze Automatic charts, please remember that the event formation may indicate an entry point for a short seller, and look at other indicators across different periods to confirm that a breakdown or reversion is likely to occur.| Cycle Indicators | ||
| Math Operators | ||
| Math Transform | ||
| Momentum Indicators | ||
| Overlap Studies | ||
| Pattern Recognition | ||
| Price Transform | ||
| Statistic Functions | ||
| Volatility Indicators | ||
| Volume Indicators |
Automatic Data Polynomial Regression Price Forecast For the 13th of January 2026
Given 90 days horizon, the Polynomial Regression forecasted value of Automatic Data Processing on the next trading day is expected to be 225.84 with a mean absolute deviation of 3.15, mean absolute percentage error of 15.53, and the sum of the absolute errors of 192.22.Please note that although there have been many attempts to predict Automatic 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 Automatic Data's next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).
Automatic Data Stock Forecast Pattern
| Backtest Automatic Data | Automatic Data Price Prediction | Buy or Sell Advice |
Automatic Data Forecasted Value
In the context of forecasting Automatic Data's 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. Automatic Data's downside and upside margins for the forecasting period are 224.59 and 227.09, respectively. We have considered Automatic Data's 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 Polynomial Regression 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 | 120.8533 |
| Bias | Arithmetic mean of the errors | None |
| MAD | Mean absolute deviation | 3.1511 |
| MAPE | Mean absolute percentage error | 0.0139 |
| SAE | Sum of the absolute errors | 192.2169 |
Predictive Modules for Automatic Data
There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as Automatic Data Processing. 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 Automatic Data's 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 Automatic Data
For every potential investor in Automatic, whether a beginner or expert, Automatic Data's price movement is the inherent factor that sparks whether it is viable to invest in it or hold it better. Automatic Stock price charts are filled with many 'noises.' These noises can hugely alter the decision one can make regarding investing in Automatic. Basic forecasting techniques help filter out the noise by identifying Automatic Data's price trends.Automatic Data 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 Automatic Data stock to make a market-neutral strategy. Peer analysis of Automatic Data could also be used in its relative valuation, which is a method of valuing Automatic Data by comparing valuation metrics with similar companies.
| Risk & Return | Correlation |
Automatic Data Processing 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 Automatic Data's 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 Automatic Data's current price.| Cycle Indicators | ||
| Math Operators | ||
| Math Transform | ||
| Momentum Indicators | ||
| Overlap Studies | ||
| Pattern Recognition | ||
| Price Transform | ||
| Statistic Functions | ||
| Volatility Indicators | ||
| Volume Indicators |
Automatic Data Market Strength Events
Market strength indicators help investors to evaluate how Automatic Data stock reacts to ongoing and evolving market conditions. The investors can use it to make informed decisions about market timing, and determine when trading Automatic Data shares will generate the highest return on investment. By undertsting and applying Automatic Data stock market strength indicators, traders can identify Automatic Data Processing entry and exit signals to maximize returns.
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. The process involves identifying the amount of risk involved in Automatic Data's investment and either accepting that risk or mitigating it. Along with some essential techniques for forecasting automatic 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 | 0.8186 | |||
| Standard Deviation | 1.25 | |||
| Variance | 1.56 |
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.
Thematic Opportunities
Explore Investment Opportunities
Additional Tools for Automatic Stock Analysis
When running Automatic Data's price analysis, check to measure Automatic Data's market volatility, profitability, liquidity, solvency, efficiency, growth potential, financial leverage, and other vital indicators. We have many different tools that can be utilized to determine how healthy Automatic Data is operating at the current time. Most of Automatic Data's value examination focuses on studying past and present price action to predict the probability of Automatic Data's future price movements. You can analyze the entity against its peers and the financial market as a whole to determine factors that move Automatic Data's price. Additionally, you may evaluate how the addition of Automatic Data to your portfolios can decrease your overall portfolio volatility.