Deere (Germany) Statistic Functions Variance

DCO Stock   420.85  8.45  1.97%   
Deere statistic functions tool provides the execution environment for running the Variance function and other technical functions against Deere. Deere value trend is the prevailing direction of the price over some defined period of time. The concept of trend is an important idea in technical analysis, including the analysis of statistic functions indicators. As with most other technical indicators, the Variance function function is designed to identify and follow existing trends. Deere statistical functions help analysts to determine different price movement patterns based on how price series statistical indicators change over time. Please specify Time Period and Deviations to execute this module.

Function
Time Period
Deviations
Execute Function
The output start index for this execution was eleven with a total number of output elements of fifty. Deere Company Variance is a measurement of the price spread between periods of Deere price series.

Deere Technical Analysis Modules

Most technical analysis of Deere help investors determine whether a current trend will continue and, if not, when it will shift. We provide a combination of tools to recognize potential entry and exit points for Deere from various momentum indicators to cycle indicators. When you analyze Deere 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.

About Deere Predictive Technical Analysis

Predictive technical analysis modules help investors to analyze different prices and returns patterns as well as diagnose historical swings to determine the real value of Deere Company. We use our internally-developed statistical techniques to arrive at the intrinsic value of Deere Company based on widely used predictive technical indicators. In general, we focus on analyzing Deere Stock price patterns and their correlations with different microeconomic environment and drivers. We also apply predictive analytics to build Deere's daily price indicators and compare them against related drivers, such as statistic functions and various other types of predictive indicators. Using this methodology combined with a more conventional technical analysis and fundamental analysis, we attempt to find the most accurate representation of Deere's intrinsic value. In addition to deriving basic predictive indicators for Deere, we also check how macroeconomic factors affect Deere price patterns. Please read more on our technical analysis page or use our predictive modules below to complement your research.
Sophisticated investors, who have witnessed many market ups and downs, anticipate that the market will even out over time. This tendency of Deere'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
419.27420.85422.43
Details
Intrinsic
Valuation
LowRealHigh
399.79401.37462.94
Details
Naive
Forecast
LowNextHigh
412.84414.42416.00
Details
Bollinger
Band Projection (param)
LowerMiddle BandUpper
350.05406.16462.26
Details

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Additional Tools for Deere Stock Analysis

When running Deere's price analysis, check to measure Deere'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 Deere is operating at the current time. Most of Deere's value examination focuses on studying past and present price action to predict the probability of Deere's future price movements. You can analyze the entity against its peers and the financial market as a whole to determine factors that move Deere's price. Additionally, you may evaluate how the addition of Deere to your portfolios can decrease your overall portfolio volatility.