Automatic Data Stock Forecast - Naive Prediction
| ADP Stock | EUR 289.95 1.70 0.59% |
The Naive Prediction forecasted value of Automatic Data Processing on the next trading day is expected to be 290.59 with a mean absolute deviation of 3.60 and the sum of the absolute errors of 219.39. Automatic Stock Forecast is based on your current time horizon. We recommend always using this module together with an analysis of Automatic Data's historical fundamentals, such as revenue growth or operating cash flow patterns.
Automatic |
Automatic Data Naive Prediction Price Forecast For the 27th of November
Given 90 days horizon, the Naive Prediction forecasted value of Automatic Data Processing on the next trading day is expected to be 290.59 with a mean absolute deviation of 3.60, mean absolute percentage error of 20.27, and the sum of the absolute errors of 219.39.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
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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 289.52 and 291.65, 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 Naive Prediction 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 | 121.1196 |
| Bias | Arithmetic mean of the errors | None |
| MAD | Mean absolute deviation | 3.5965 |
| MAPE | Mean absolute percentage error | 0.0136 |
| SAE | Sum of the absolute errors | 219.3853 |
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.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.
| Daily Balance Of Power | 9.2 T | |||
| Rate Of Daily Change | 1.01 | |||
| Day Median Price | 289.95 | |||
| Day Typical Price | 289.95 | |||
| Price Action Indicator | 0.85 | |||
| Period Momentum Indicator | 1.7 |
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 | 0.8669 | |||
| Semi Deviation | 0.4481 | |||
| Standard Deviation | 1.06 | |||
| Variance | 1.13 | |||
| Downside Variance | 0.6152 | |||
| Semi Variance | 0.2008 | |||
| Expected Short fall | (1.06) |
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.
Currently Active Assets on Macroaxis
Additional Information and Resources on Investing in Automatic Stock
When determining whether Automatic Data Processing is a good investment, qualitative aspects like company management, corporate governance, and ethical practices play a significant role. A comparison with peer companies also provides context and helps to understand if Automatic Stock is undervalued or overvalued. This multi-faceted approach, blending both quantitative and qualitative analysis, forms a solid foundation for making an informed investment decision about Automatic Data Processing Stock. Highlighted below are key reports to facilitate an investment decision about Automatic Data Processing Stock:Check out Historical Fundamental Analysis of Automatic Data to cross-verify your projections. You can also try the Financial Widgets module to easily integrated Macroaxis content with over 30 different plug-and-play financial widgets.