Automatic Data Stock Forecast - Double Exponential Smoothing
| ADP Stock | USD 254.51 3.66 1.42% |
Automatic Stock outlook is based on your current time horizon. Although Automatic Data's naive historical forecasting may sometimes provide an important future outlook for the firm, we recommend always cross-verifying it against solid analysis of Automatic Data's systematic risk associated with finding meaningful patterns of Automatic Data fundamentals over time.
As of today, the relative strength index (RSI) of Automatic Data's share price is approaching 45. This suggests that the stock is in nutural position, most likellhy at or near its support level. The main point of RSI analysis is to track how fast people are buying or selling Automatic Data, making its price go up or down. Momentum 45
Impartial
Oversold | Overbought |
Quarterly Earnings Growth 0.064 | EPS Estimate Next Quarter 2.6041 | EPS Estimate Current Year 10.9158 | EPS Estimate Next Year 11.9285 | Wall Street Target Price 289.7857 |
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. We also analyze overall investor sentiment towards Automatic Data using Automatic Data's stock options and short interest. It helps to benchmark the overall future attitude of investors towards Automatic using crowd psychology based on the activity and movement of Automatic Data's stock price.
Automatic Data Short Interest
An investor who is long Automatic Data may also wish to track short interest. As short interest increases, investors should be becoming more worried about Automatic Data and may potentially protect profits, hedge Automatic Data with its derivative instruments, or be ready for some potential downside.
200 Day MA 288.6535 | Short Percent 0.0192 | Short Ratio 2.78 | Shares Short Prior Month 7.6 M | 50 Day MA 258.1124 |
Automatic Relative Strength Index
The Double Exponential Smoothing forecasted value of Automatic Data Processing on the next trading day is expected to be 254.93 with a mean absolute deviation of 2.13 and the sum of the absolute errors of 127.99.Automatic Data Processing Hype to Price Pattern
Investor biases related to Automatic Data's public news can be used to forecast risks associated with an investment in Automatic. The trend in average sentiment can be used to explain how an investor holding Automatic can time the market purely based on public headlines and social activities around Automatic Data Processing. Please note that most equities that are difficult to arbitrage are affected by market sentiment the most.
Some investors profit by finding stocks that are overvalued or undervalued based on market sentiment. The correlation of Automatic Data's market sentiment to its price can help taders to make decisions based on the overall investors consensus about Automatic Data.
Automatic Data Implied Volatility | 0.54 |
Automatic Data's implied volatility exposes the market's sentiment of Automatic Data Processing stock's possible movements over time. However, it does not forecast the overall direction of its price. In a nutshell, if Automatic Data's implied volatility is high, the market thinks the stock has potential for high price swings in either direction. On the other hand, the low implied volatility suggests that Automatic Data stock will not fluctuate a lot when Automatic Data's options are near their expiration.
The Double Exponential Smoothing forecasted value of Automatic Data Processing on the next trading day is expected to be 254.93 with a mean absolute deviation of 2.13 and the sum of the absolute errors of 127.99. Automatic Data after-hype prediction price | USD 254.25 |
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 | Build AI portfolio with Automatic Stock |
Prediction based on Rule 16 of the current Automatic contract
Based on the Rule 16, the options market is currently suggesting that Automatic Data Processing will have an average daily up or down price movement of about 0.0338% per day over the life of the 2026-03-20 option contract. With Automatic Data trading at USD 254.51, that is roughly USD 0.0859 . If you think that the market is fully incorporating Automatic Data's daily price movement you should consider acquiring Automatic Data Processing options at the current volatility level of 0.54%. But if you have an opposite viewpoint you should avoid it and even consider selling them.
Open Interest Against 2026-03-20 Automatic Option Contracts
Although open interest is a measure utilized in the options markets, it could be used to forecast Automatic Data's spot prices because the number of available contracts in the market changes daily, and new contracts can be created or liquidated at will. Since open interest in Automatic Data's options reflects these daily shifts, investors could use the patterns of these changes to develop long and short-term trading strategies for Automatic Data stock based on available contracts left at the end of a trading day.
Please note that to derive more accurate forecasting about market movement from the current Automatic Data's open interest, investors have to compare it to Automatic Data's spot prices. As Ford's stock price increases, high open interest indicates that money is entering the market, and the market is strongly bullish. Conversely, if the price of Automatic Data is decreasing and there is high open interest, that is a sign that the bearish trend will continue, and investors may react by taking short positions in Automatic. So, decreasing or low open interest during a bull market indicates that investors are becoming uncertain of the depth of the bullish trend, and a reversal in sentiment will likely follow.
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 Double Exponential Smoothing Price Forecast For the 29th of January
Given 90 days horizon, the Double Exponential Smoothing forecasted value of Automatic Data Processing on the next trading day is expected to be 254.93 with a mean absolute deviation of 2.13, mean absolute percentage error of 7.73, and the sum of the absolute errors of 127.99.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 253.84 and 256.02, 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 Double Exponential Smoothing 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 | Huge |
| Bias | Arithmetic mean of the errors | 0.4575 |
| MAD | Mean absolute deviation | 2.1332 |
| MAPE | Mean absolute percentage error | 0.0083 |
| SAE | Sum of the absolute errors | 127.9924 |
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.
Automatic Data After-Hype Price Density Analysis
As far as predicting the price of Automatic Data at your current risk attitude, this probability distribution graph shows the chance that the prediction will fall between or within a specific range. We use this chart to confirm that your returns on investing in Automatic Data or, for that matter, your successful expectations of its future price, cannot be replicated consistently. Please note, a large amount of money has been lost over the years by many investors who confused the symmetrical distributions of Stock prices, such as prices of Automatic Data, with the unreliable approximations that try to describe financial returns.
Next price density |
| Expected price to next headline |
Automatic Data Estimiated After-Hype Price Volatility
In the context of predicting Automatic Data's stock value on the day after the next significant headline, we show statistically significant boundaries of downside and upside scenarios based on Automatic Data's historical news coverage. Automatic Data's after-hype downside and upside margins for the prediction period are 253.16 and 255.34, respectively. We have considered Automatic Data's daily market price in relation to the headlines to evaluate this method's predictive performance. Remember, however, there is no scientific proof or empirical evidence that news-based prediction models outperform traditional linear, nonlinear models or artificial intelligence models to provide accurate predictions consistently.
Current Value
Automatic Data is very steady at this time. Analysis and calculation of next after-hype price of Automatic Data Processing is based on 3 months time horizon.
Automatic Data Stock Price Outlook Analysis
Have you ever been surprised when a price of a Company such as Automatic Data is soaring high without any particular reason? This is usually happening because many institutional investors are aggressively trading Automatic Data backward and forwards among themselves. Have you ever observed a lot of a particular company's price movement is driven by press releases or news about the company that has nothing to do with actual earnings? Usually, hype to individual companies acts as price momentum. If not enough favorable publicity is forthcoming, the Stock price eventually runs out of speed. So, the rule of thumb here is that as long as this news hype has nothing to do with immediate earnings, you should pay more attention to it. If you see this tendency with Automatic Data, there might be something going there, and it might present an excellent short sale opportunity.
| Expected Return | Period Volatility | Hype Elasticity | Related Elasticity | News Density | Related Density | Expected Hype |
0.03 | 1.09 | 0.26 | 0.04 | 21 Events / Month | 8 Events / Month | In about 21 days |
| Latest traded price | Expected after-news price | Potential return on next major news | Average after-hype volatility | |
254.51 | 254.25 | 0.10 |
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Automatic Data Hype Timeline
On the 28th of January Automatic Data Processing is traded for 254.51. The entity has historical hype elasticity of -0.26, and average elasticity to hype of competition of 0.04. Automatic is forecasted to decline in value after the next headline, with the price expected to drop to 254.25. The average volatility of media hype impact on the company price is about 12.63%. The price depreciation on the next news is expected to be -0.1%, whereas the daily expected return is presently at -0.03%. The volatility of related hype on Automatic Data is about 76.65%, with the expected price after the next announcement by competition of 254.55. About 87.0% of the company shares are held by institutions such as insurance companies. The company recorded earning per share (EPS) of 10.12. Automatic Data Processing last dividend was issued on the 13th of March 2026. The entity had 1139:1000 split on the 1st of October 2014. Considering the 90-day investment horizon the next forecasted press release will be in about 21 days. Check out Historical Fundamental Analysis of Automatic Data to cross-verify your projections.Automatic Data Related Hype Analysis
Having access to credible news sources related to Automatic Data's direct competition is more important than ever and may enhance your ability to predict Automatic Data's future price movements. Getting to know how Automatic Data's peers react to changing market sentiment, related social signals, and mainstream news is a great way to find investing opportunities and time the market. The summary table below summarizes the essential lagging indicators that can help you analyze how Automatic Data may potentially react to the hype associated with one of its peers.
| HypeElasticity | NewsDensity | SemiDeviation | InformationRatio | PotentialUpside | ValueAt Risk | MaximumDrawdown | |||
| PAYX | Paychex | (0.24) | 5 per month | 0.00 | (0.24) | 2.32 | (3.15) | 6.15 | |
| PH | Parker Hannifin | 8.20 | 8 per month | 0.62 | 0.18 | 2.46 | (1.57) | 9.94 | |
| LMT | Lockheed Martin | 10.17 | 6 per month | 1.32 | 0.15 | 2.91 | (2.25) | 7.74 | |
| GD | General Dynamics | 4.60 | 7 per month | 1.17 | 0.04 | 2.00 | (2.00) | 7.72 | |
| TT | Trane Technologies plc | 2.26 | 11 per month | 0.00 | (0.09) | 2.26 | (2.74) | 7.87 | |
| MMM | 3M Company | (1.24) | 7 per month | 0.00 | (0.1) | 2.11 | (2.10) | 9.58 | |
| NOC | Northrop Grumman | 5.19 | 7 per month | 1.42 | 0.08 | 2.85 | (1.69) | 9.88 | |
| DE | Deere Company | (8.63) | 22 per month | 1.51 | 0.07 | 2.70 | (2.07) | 7.96 | |
| HON | Honeywell International | (7.08) | 17 per month | 0.96 | 0.09 | 2.22 | (1.97) | 9.46 | |
| UPS | United Parcel Service | 0.99 | 7 per month | 1.04 | 0.16 | 2.75 | (2.40) | 11.51 |
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 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.
| Accumulation Distribution | 54353.21 | |||
| Daily Balance Of Power | (0.91) | |||
| Rate Of Daily Change | 0.99 | |||
| Day Median Price | 255.45 | |||
| Day Typical Price | 255.14 | |||
| Price Action Indicator | (2.77) | |||
| Period Momentum Indicator | (3.66) | |||
| Relative Strength Index | 45.47 |
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 | 0.9106 | |||
| Standard Deviation | 1.31 | |||
| Variance | 1.73 |
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
The number of cover stories for Automatic Data depends on current market conditions and Automatic Data's risk-adjusted performance over time. The coverage that generates the most noise at a given time depends on the prevailing investment theme that Automatic Data is classified under. However, while its typical story may have numerous social followers, the rapid visibility can also attract short-sellers, who usually are skeptical about Automatic Data's long-term prospects. So, having above-average coverage will typically attract above-average short interest, leading to significant price volatility.
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Automatic Data Short Properties
Automatic Data's future price predictability will typically decrease when Automatic Data's long traders begin to feel the short-sellers pressure to drive the price lower. The predictive aspect of Automatic Data Processing often depends not only on the future outlook of the potential Automatic Data's investors but also on the ongoing dynamics between investors with different trading styles. Because the market risk indicators may have small false signals, it is better to identify suitable times to hedge a portfolio using different long/short signals. Automatic Data's indicators that are reflective of the short sentiment are summarized in the table below.
| Common Stock Shares Outstanding | 408.7 M | |
| Cash And Short Term Investments | 7.8 B |
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.