Applied Industrial Stock Forecast - Polynomial Regression

Applied Industrial polinomial regression implements a single variable polynomial regression model using the daily prices as the independent variable. The coefficients of the regression for Applied Industrial Technologies as well as the accuracy indicators are determined from the period prices.

Applied Industrial Polynomial Regression Price Forecast For the 29th of November

Given 90 days horizon, the Polynomial Regression forecasted value of Applied Industrial Technologies on the next trading day is expected to be 290.35 with a mean absolute deviation of 6.23, mean absolute percentage error of 62.93, and the sum of the absolute errors of 379.90.

Applied Industrial Stock Forecast Pattern

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Applied Industrial Price Prediction

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Applied Industrial Forecasted Value

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 Applied Industrial stock data series using in forecasting. Note that when a statistical model is used to represent Applied Industrial 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. A single variable polynomial regression model attempts to put a curve through the Applied Industrial historical price points. Mathematically, assuming the independent variable is X and the dependent variable is Y, this line can be indicated as: Y = a₀ + a₁*X + a₂*X² + a₃*X³ + ... + a_m*X^m

Predictive Modules for Applied Industrial

There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as Applied Industrial. 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.

Hype Prediction Low Estimated High 270.51 272.78 275.05 Details

Intrinsic Valuation Low Real High 238.42 240.69 301.29 Details

8 Analysts Consensus Low Target High 164.10 180.33 200.17 Details

Earnings Estimates (0) Low Projected EPS High 2.16 2.21 2.25 Details

Other Forecasting Options for Applied Industrial

For every potential investor in Applied, whether a beginner or expert, Applied Industrial's price movement is the inherent factor that sparks whether it is viable to invest in it or hold it better. Applied Stock price charts are filled with many 'noises.' These noises can hugely alter the decision one can make regarding investing in Applied. Basic forecasting techniques help filter out the noise by identifying Applied Industrial's price trends.

Applied Industrial Related Equities

Risk & Return

Correlation

Applied Industrial 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 Applied Industrial'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 Applied Industrial's current price.

Applied Industrial Market Strength Events

Applied Industrial Risk Indicators

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