Automatic Data Processing Stock Probability of Future Stock Price Finishing Under 306.03

ADP Stock  USD 300.75  1.48  0.49%   
Automatic Data's implied volatility is one of the determining factors in the pricing options written on Automatic Data Processing. Implied volatility approximates the future value of Automatic Data based on the option's current value. Options with high implied volatility have higher premiums and can be used to hedge the downside of investing in Automatic Data Processing over a specific time period. For example, ADP Option Call 13-12-2024 300 is a CALL option contract on Automatic Data's common stock with a strick price of 300.0 expiring on 2024-12-13. The contract was last traded on 2024-12-10 at 15:59:47 for $3.2 and, as of today, has 2 days remaining before the expiration. The option is currently trading at an ask price of $0.0. The implied volatility as of the 11th of December 2024 is 2.0. View All Automatic options

Closest to current price Automatic long CALL Option Payoff at Expiration

Automatic Data's future price is the expected price of Automatic Data instrument. It is based on its current growth rate as well as the projected cash flow expected by the investors. This tool provides a mechanism to make assumptions about the upside potential and downside risk of Automatic Data Processing performance during a given time horizon utilizing its historical volatility. Check out Automatic Data Backtesting, Automatic Data Valuation, Automatic Data Correlation, Automatic Data Hype Analysis, Automatic Data Volatility, Automatic Data History as well as Automatic Data Performance.
  
At this time, Automatic Data's Price To Sales Ratio is relatively stable compared to the past year. As of 12/11/2024, Price Book Value Ratio is likely to grow to 22.34, while Price To Free Cash Flows Ratio is likely to drop 17.57. Please specify Automatic Data's target price for which you would like Automatic Data odds to be computed.

Automatic Data Target Price Odds to finish below 306.03

The tendency of Automatic Stock price to converge on an average value over time is a known aspect in finance that investors have used since the beginning of the stock market for forecasting. However, many studies suggest that some traded equity instruments are consistently mispriced before traders' demand and supply correct the spread. One possible conclusion to this anomaly is that these stocks have additional risk, for which investors demand compensation in the form of extra returns.
Current PriceHorizonTarget PriceOdds to stay under $ 306.03  after 90 days
 300.75 90 days 306.03 
about 91.2
Based on a normal probability distribution, the odds of Automatic Data to stay under $ 306.03  after 90 days from now is about 91.2 (This Automatic Data Processing probability density function shows the probability of Automatic Stock to fall within a particular range of prices over 90 days) . Probability of Automatic Data Processing price to stay between its current price of $ 300.75  and $ 306.03  at the end of the 90-day period is about 10.18 .
Considering the 90-day investment horizon Automatic Data has a beta of 0.8. This suggests as returns on the market go up, Automatic Data average returns are expected to increase less than the benchmark. However, during the bear market, the loss on holding Automatic Data Processing will be expected to be much smaller as well. Additionally Automatic Data Processing has an alpha of 0.0504, implying that it can generate a 0.0504 percent excess return over Dow Jones Industrial after adjusting for the inherited market risk (beta).
   Automatic Data Price Density   
       Price  

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.
Hype
Prediction
LowEstimatedHigh
301.00302.00303.00
Details
Intrinsic
Valuation
LowRealHigh
272.01309.79310.79
Details
Naive
Forecast
LowNextHigh
295.05296.05297.04
Details
19 Analysts
Consensus
LowTargetHigh
236.54259.93288.52
Details

Automatic Data Risk Indicators

For the most part, the last 10-20 years have been a very volatile time for the stock market. Automatic Data is not an exception. The market had few large corrections towards the Automatic Data's value, including both sudden drops in prices as well as massive rallies. These swings have made and broken many portfolios. An investor can limit the violent swings in their portfolio by implementing a hedging strategy designed to limit downside losses. If you hold Automatic Data Processing, one way to have your portfolio be protected is to always look up for changing volatility and market elasticity of Automatic Data within the framework of very fundamental risk indicators.
α
Alpha over Dow Jones
0.05
β
Beta against Dow Jones0.80
σ
Overall volatility
11.05
Ir
Information ratio 0.03

Automatic Data Alerts and Suggestions

In today's market, stock alerts give investors the competitive edge they need to time the market and increase returns. Checking the ongoing alerts of Automatic Data for significant developments is a great way to find new opportunities for your next move. Suggestions and notifications for Automatic Data Processing can help investors quickly react to important events or material changes in technical or fundamental conditions and significant headlines that can affect investment decisions.
Automatic Data Processing has 3.71 B in debt with debt to equity (D/E) ratio of 1.4, which is OK given its current industry classification. Automatic Data Processing has a current ratio of 0.95, suggesting that it has not enough short term capital to pay financial commitments when the payables are due. Note however, debt could still be an excellent tool for Automatic to invest in growth at high rates of return.
Over 84.0% of Automatic Data shares are held by institutions such as insurance companies
Latest headline from news.google.com: Geode Capital Management LLC Grows Stock Holdings in Automatic Data Processing, Inc. - MarketBeat

Automatic Data Price Density Drivers

Market volatility will typically increase when nervous long traders begin to feel the short-sellers pressure to drive the market lower. The future price of Automatic Stock often depends not only on the future outlook of the current and 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 Outstanding412.2 M
Cash And Short Term Investments2.9 B

Automatic Data Technical Analysis

Automatic Data's future price can be derived by breaking down and analyzing its technical indicators over time. Automatic Stock technical analysis helps investors analyze different prices and returns patterns as well as diagnose historical swings to determine the real value of Automatic Data Processing. In general, you should focus on analyzing Automatic Stock price patterns and their correlations with different microeconomic environments and drivers.

Automatic Data Predictive Forecast Models

Automatic Data's time-series forecasting models is one of many Automatic Data's stock analysis techniques aimed to predict future share value based on previously observed values. Time-series forecasting models are widely used for non-stationary data. Non-stationary data are called the data whose statistical properties, e.g., the mean and standard deviation, are not constant over time, but instead, these metrics vary over time. This non-stationary Automatic Data's historical data is usually called time series. Some empirical experimentation suggests that the statistical forecasting models outperform the models based exclusively on fundamental analysis to predict the direction of the stock market movement and maximize returns from investment trading.

Things to note about Automatic Data Processing

Checking the ongoing alerts about Automatic Data for important developments is a great way to find new opportunities for your next move. Our stock alerts and notifications screener for Automatic Data Processing help investors to be notified of important events, changes in technical or fundamental conditions, and significant headlines that can affect investment decisions.
Automatic Data Processing has 3.71 B in debt with debt to equity (D/E) ratio of 1.4, which is OK given its current industry classification. Automatic Data Processing has a current ratio of 0.95, suggesting that it has not enough short term capital to pay financial commitments when the payables are due. Note however, debt could still be an excellent tool for Automatic to invest in growth at high rates of return.
Over 84.0% of Automatic Data shares are held by institutions such as insurance companies
Latest headline from news.google.com: Geode Capital Management LLC Grows Stock Holdings in Automatic Data Processing, Inc. - MarketBeat

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.